ObjectiveHyperspectral images （HSIs） capture reflectance values across hundreds of contiguous spectral bands at each spatial location， providing rich spectral–spatial information that enables precise discrimination of materials with subtle spectral differences. This high spectral resolution makes HSIs particularly valuable for environmental monitoring， land cover mapping， precision agriculture， and change detection applications. Change detection （CD） in multitemporal HSIs aims to identify meaningful surface alterations by comparing images of the same scene acquired at different times. Accurate HSI-CD plays a critical role in ecological monitoring， urban expansion analysis， and disaster assessment. However， the task faces significant challenges， including high dimensionality， spectral redundancy， spatial heterogeneity， and the difficulty of effectively integrating spatial， spectral， and temporal features. Traditional methods， such as change vector analysis （CVA）， principal component analysis （PCA）， and multivariate alteration detection （MAD）， rely on hand-crafted features and empirical thresholding. Although computationally efficient， these approaches are sensitive to noise， illumination variations， and atmospheric effects. Deep learning methods， particularly convolutional neural networks （CNNs）， have demonstrated superior performance by automatically learning hierarchical features. Most CNN-based frameworks adopt dual-branch Siamese architectures， yet they still struggle to capture long-range dependencies and explicit temporal dynamics.MethodTo address these limitations， a novel Time-Difference-Guided Network （TDG-Net） is proposed. The method employs a Siamese architecture with Vision Mamba as the feature extraction backbone to efficiently capture long-range spatial dependencies with linear computational complexity. Two core components are integrated： the Time Storage Module （TSM） and the temporal difference guidance strategy. Bi-temporal HSIs are first fed into the dual-branch Vision Mamba backbone to extract multi-level spatial–spectral features. The TSM performs sequential temporal modeling on these features using a simplified long short-term memory （sLSTM）. To reduce computational cost， spectral compression via 1×1 convolution and spatial down-sampling via max pooling are applied before feeding features into the sLSTM. The sLSTM generates explicit temporal difference representations. The temporal difference guidance strategy then converts these low-resolution difference features into spatial attention weights through bilinear up-sampling and ReLU activation. These weights are fed back to the dual-branch network via residual connections， adaptively emphasizing change-relevant regions and suppressing unchanged areas at each hierarchical level. Finally， the enhanced multi-level features are fused and passed to the classification head to produce the binary change detection map. To mitigate severe class imbalance， Dice Loss is employed instead of conventional cross-entropy loss， directly optimizing the overlap between predicted and ground-truth change regions. Compared with recent Mamba-based methods， the core distinction of TDG-Net lies in its explicit modeling and hierarchical guidance of temporal differences across multiple feature levels via a lightweight TSM and residual feedback， rather than relying primarily on high-level implicit fusion.ResultComprehensive experiments were conducted on three widely used benchmark hyperspectral datasets： River， Farmland， and Hermiston. All experiments were implemented on an NVIDIA RTX 3090 GPU using TensorFlow-GPU 2.5.0. Performance was evaluated using Overall Accuracy （OA） and Kappa coefficient， with results averaged over 10 independent runs. Ablation studies confirmed the significant contributions of the TSM and temporal difference guidance strategy. Comparative experiments against state-of-the-art methods， demonstrated that TDG-Net consistently outperforms all competitors. On the River dataset， TDG-Net achieved an OA of 96.54% and a Kappa of 78.51%. On the Hermiston dataset， it reached an OA of 98.21% and a Kappa of 91.79%. On the Farmland dataset， it attained an OA of 95.87% and a Kappa of 90.11%. Additional analysis on model complexity shows that TDG-Net maintains competitive parameter count， FLOPs， and inference time while achieving superior accuracy.ConclusionThe proposed TDG-Net effectively addresses the key limitations of existing hyperspectral image change detection methods by explicitly modeling temporal differences at multiple hierarchical levels and guiding feature learning with adaptive attention. By integrating a lightweight Time Storage Module based on simplified LSTM and a temporal difference guidance strategy within a Vision Mamba backbone， the method captures rich spatio-spectral-temporal dynamics while effectively suppressing pseudo-changes. The introduction of Dice Loss further alleviates the severe class imbalance problem. Extensive experiments on three benchmark datasets demonstrate that TDG-Net achieves state-of-the-art performance in both quantitative metrics and visual quality， exhibiting strong robustness and generalization in complex scenarios. Compared with existing Mamba-based methods such as SAVDGN and CDMamba， which primarily rely on implicit high-level fusion， TDG-Net explicitly models and hierarchically guides temporal differences across multiple feature levels through a lightweight TSM and residual feedback mechanism， achieving more fine-grained change representation.

ObjectiveAerial RGB-IR object detection has received increasing attention in remote sensing because visible and infrared images provide complementary information under complex imaging conditions. Visible images preserve rich texture， color， and structural details， whereas infrared images are less sensitive to low illumination and can highlight thermal targets at night or in low-light scenes. However， the contribution of each modality changes with imaging factors， including illumination， exposure status， texture clarity， target-background contrast， background clutter， and artificial light interference. Existing methods have improved detection performance through cross-modal alignment， feature interaction， attention reweighting， and multi-scale fusion. Nevertheless， most of them still focus on feature-level fusion and lack explicit modeling of modality reliability. In other words， they do not sufficiently estimate how reliable each modality is under the current imaging condition， or how much each modality should contribute to detection. Some language-guided and condition-aware methods introduce semantic cues into multimodal detection. However， they usually rely on coarse scene descriptions， category-level prompts， or additional large-model branches during inference. These strategies are insufficient for characterizing modality quality attributes that are directly related to detection reliability. They may also increase the deployment burden on computation-constrained aerial platforms. To address these issues， this paper proposes a modality reliability modeling method for aerial RGB-IR object detection. The method transfers modality quality perception from a vision-language model to the detector during training and enables adaptive multimodal fusion without additional large-model inference cost.MethodsThe proposed method consists of structured modality quality description， semantic prior distillation， and reliability-aware adaptive fusion. First， a modality quality attribute description dataset is constructed for UAV-oriented aerial scenes. It provides structured supervision for modality reliability learning. Instead of using only category labels or coarse scene tags， the annotation scheme explicitly describes key imaging factors that affect detection performance in both modalities. For the RGB modality， the attributes include illumination condition， exposure status， texture clarity， artificial light interference， and background clutter. For the infrared modality， the attributes include target-background contrast， boundary clarity， and background cleanliness. Second， a vision-language model is used to encode the modality quality descriptions and generate semantic priors related to RGB and infrared reliability. These priors are used only during training. By combining semantic distillation with attribute supervision， the detector is guided to learn detection-oriented reliability representations. Thus， modality quality perception is internalized into the visual detection network. Third， a global-local adaptive fusion mechanism is designed based on the learned reliability representations. Global scene reliability captures the overall effectiveness of RGB and infrared cues under the current imaging condition. Local spatial reliability further adjusts the modality contribution at different spatial positions. Therefore， the detector can dynamically fuse RGB and infrared features according to both scene-level and region-level reliability. Since semantic priors are used only during training， the proposed framework does not require additional text prompts， language branches， or large-model participation during inference.ResultsExperiments are conducted on DroneVehicle and VEDAI， two public aerial RGB-IR object detection datasets. On DroneVehicle， the proposed method achieves 79.7% mAP@0.5 and 53.7% mAP@0.5：0.95. On VEDAI， it achieves 67.1% mAP@0.5 and 30.1% mAP@0.5：0.95. The method also shows stronger robustness in challenging scenarios， especially under nighttime， low-light， and complex interference conditions. Ablation studies verify the effectiveness of modality quality attribute modeling， semantic prior distillation， and joint global-local modality reliability modeling. In addition， the proposed method maintains good inference efficiency because no extra large-model branch is introduced during testing.ConclusionThis paper presents a modality reliability modeling method for aerial RGB-IR object detection. The method uses a vision-language model only during training to encode modality quality descriptions and provide semantic supervision. Through semantic distillation and attribute supervision， the detector learns reliability-aware representations. By jointly modeling global scene reliability and local spatial reliability， the detector can adaptively adjust the contributions of visible and infrared modalities under varying imaging conditions. Experimental results on DroneVehicle and VEDAI demonstrate that the proposed method improves detection accuracy and robustness， especially in nighttime， low-light， and cluttered scenes.

Wildfires occur frequently worldwide and pose severe threats to ecosystem stability， regional climate systems， and human life and property. In recent years， against the backdrop of global warming， adverse meteorological conditions such as extreme heat， prolonged drought， and anomalous wind fields have become more frequent， further increasing the risk of wildfires. In particular， under the combined influence of complex terrain， hot and dry winds， and abrupt meteorological changes， fires can rapidly evolve from weak initial thermal anomalies into large-scale disasters， causing forest resource loss， increased carbon emissions， ecological degradation， and severe socioeconomic impacts. Therefore， achieving rapid and accurate detection of early wildfire events over large spatial extents has become an important research objective in remote-sensing-based disaster monitoring and a key issue for improving early warning and emergency response capabilities. Compared with ground-based monitoring and airborne observations， satellite remote sensing provides broad spatial coverage， high observation efficiency， and continuous monitoring capability over large regions. Among available platforms， geostationary meteorological satellites are particularly valuable for near-real-time wildfire monitoring because they can repeatedly observe the same region at short temporal intervals. Compared with polar-orbiting satellites， Himawari-8/9 provides continuous observations with a temporal resolution of 10 min and 16 multispectral channels， making it an important data source for operational wildfire monitoring. Benefiting from this high-frequency temporal sampling capability， Himawari-8/9 can capture thermal anomaly responses at the moment of fire ignition. Furthermore， it continuously characterizes the dynamic evolution of fire pixels from emergence to intensification and expansion. This capability is especially important for early wildfire detection. Many early fire pixels are extremely small， exhibit weak thermal anomaly signals， and are not visually salient in a single image. Their identification often depends on variation trends and cumulative characteristics across continuous time series. Existing fire detection methods based on Himawari-8/9 data mainly include traditional threshold-based methods， spatial contextual approaches， and， more recently， deep-learning-based methods. Traditional multi-temporal threshold methods and contextual algorithms usually rely on manually designed decision rules derived from brightness temperature， band differences， and neighborhood background statistics. These methods are physically interpretable， easy to implement， and computationally efficient， and therefore perform well in some typical scenarios. However， they remain prone to false alarms and missed detections. This is especially true under complex surface backgrounds， cloud contamination， smoke interference， and extremely weak thermal anomalies in the early stage of wildfire development. For example， bare land， high-albedo surfaces， urban heat sources， cloud edges， and terrain shadows may produce radiometric responses similar to those of fire pixels， thereby weakening the discriminative capability of rule-based methods. In addition， fixed thresholds or semi-empirical rules often lack sufficient generalization across different regions， seasons， and land-cover types. With the rapid development of deep learning， convolutional neural networks， recurrent neural networks， and Transformer-based models have gradually been introduced into wildfire detection tasks. Compared with traditional methods， these approaches can automatically learn more discriminative spatial and temporal features in a data-driven manner， thereby reducing dependence on handcrafted rules and improving adaptability in complex scenes. Nevertheless， they still exhibit clear limitations for early wildfire detection based on long time-series Himawari-8/9 imagery. Convolutional neural networks mainly emphasize local spatial representations and remain limited in modeling long-term dynamic dependencies during wildfire evolution. Recurrent neural networks and their variants possess sequence modeling capability， but when handling long sequences at high temporal resolution， they are prone to gradient vanishing and long-term memory decay. This issue becomes more pronounced in scenarios involving 24 h of continuous observation and 144 temporal steps. Transformers can model global dependencies and long-range feature interactions. However， their computational complexity generally increases quadratically with sequence length. This imposes considerable efficiency pressure on near-real-time geostationary satellite monitoring. Therefore， early wildfire detection from long time-series Himawari-8/9 imagery still requires an efficient framework that can simultaneously capture weak spatial thermal anomalies and model long-range temporal evolution. To address these challenges， this study proposes Fire-Mamba， an early wildfire detection framework that integrates multi-scale spatial representation with long-sequence dynamic modeling. Fire-Mamba is designed for early wildfire targets in Himawari-8/9 imagery. These targets are typically extremely small， weak in thermal radiation variation， and highly susceptible to complex background interference. The framework aims to improve the accuracy and stability of near-real-time wildfire detection while maintaining manageable computational cost. Its design is motivated by two observations. First， early wildfire pixels usually manifest as subtle local thermal anomalies and lack salient shape or texture cues. Second， genuine fire pixels often cannot be reliably distinguished from transient background disturbances using a single image. Instead， their identification depends on variation trends across continuous observations. Specifically， the B07， B14， and B03 bands are selected to construct multispectral temporal inputs， and day–night cloud masking is performed using multi-band threshold rules to reduce interference from clouds and complex backgrounds. According to the 10 min observation interval of Himawari-8/9， a continuous sequence of length 144 is constructed to represent a 24 h temporal window， thereby providing sufficient support for modeling wildfire dynamics. Compared with short-window strategies， such a long time-series input can reflect not only the instantaneous thermal anomaly state of candidate fire pixels， but also their continuous intensification， spatial expansion， and temporal persistence. In the spatial feature extraction stage， a Multi-Scale Contextual Thermal Anomaly-aware Module （MCTAM） module is designed to enhance the representation of weak fire-induced thermal anomalies under complex backgrounds. The MCTAM module employs a 3 × 3 depthwise separable convolution to extract local thermal-gradient features induced by weak fire signals. This enables the capture of fine-grained local brightness-temperature variations and spatial discontinuities around candidate fire pixels. Meanwhile， a 7 × 7 convolution branch is introduced to capture broader background contextual information. This branch suppresses pseudo-anomalous responses caused by non-fire heat sources， heterogeneous land-cover patterns， or complex textures. Global average pooling and a multilayer perceptron are further used to generate channel-wise weights for adaptive feature recalibration， enabling the model to enhance fire-related features while suppressing background interference and isolated noise. In the temporal modeling stage， the selective state-space model Mamba is introduced to perform long-range dynamic modeling on pixel-wise temporal features， thereby improving the recognition of weak early thermal anomalies and their evolutionary trends. Compared with conventional recurrent structures， Mamba maintains strong temporal modeling capability while processing long sequences more efficiently. This makes it highly suitable for high-temporal-resolution continuous observations from geostationary satellites. By modeling long-range dependencies in pixel-wise temporal features， Fire-Mamba better captures the persistence， intensification， and diffusion characteristics of fire signals over time. Consequently， it improves the discrimination between genuine wildfire signals and short-term background fluctuations. Finally， to address class imbalance caused by the sparsity of fire pixels， Focal Loss is adopted at the prediction stage to encourage the model to focus more on hard samples and improve fire-class detection performance. Experimental results demonstrate that Fire-Mamba achieves superior performance across multiple quantitative metrics. Its average fire detection rate （FA） reaches 90.33%， significantly outperforming the second-best CNN-LSTM model （82.28%） and the official JAXA WLF L2 product （40.66%）. The model also achieves an overall accuracy （OA） of 99.60%， while reducing the average omission rate to 9.67%， indicating high sensitivity to small fire pixels. Compared with most deep-learning baselines， Fire-Mamba reduces isolated false alarms while maintaining higher fire detection sensitivity. However， its FAR remains higher than that of the conservative JAXA WLF L2 product， indicating that false-alarm suppression still requires further improvement. In summary， Fire-Mamba achieves a lightweight integration of multi-scale spatial thermal-gradient perception and long-range temporal evolution modeling， providing strong technical support for large-scale near-real-time wildfire monitoring and early warning.

Against the profound backdrop of the advancing global "Dual Carbon" strategy and the rapid proliferation of the visual Artificial Intelligence （AI） industry， promoting the green transition of visual AI technologies has emerged as a crucial pathway toward sustainable socioeconomic development. National directives， such as China’s "14th Five-Year Plan for Digital Economy Development" explicitly emphasize the necessity of advancing green and low-carbon computing infrastructures. In recent years， the revolutionary leaps in visual AI performance have been fundamentally driven by the "Scaling Law，" which dictates that model capability scales in direct proportion to continuous expansions in model parameters and the exponential growth of training data.While this brute-force trajectory has unlocked unprecedented capabilities in complex perception tasks， it has simultaneously triggered severe resource bottlenecks. The prohibitive financial and temporal costs associated with massive physical data collection and fine-grained manual annotation， coupled with the exorbitant energy consumption required for training and deploying massive architectures， pose stark challenges to the low-carbon transformation of the AI industry. In response to this impending crisis， "Green Visual AI" has garnered widespread attention as a pivotal research paradigm. The core objective of this study is to systematically review energy-efficient methodologies that harmonize technical performance with long-term ecological sustainability. This paper aims to provide a comprehensive theoretical foundation and practical framework by exploring optimization strategies that minimize data， computational， and human resource requirements across the entire lifecycle of visual AI systems. This review adopts a comprehensive survey methodology to systematically deconstruct the entire lifecycle of visual intelligence models. We construct a structured taxonomy organized around four core， interrelated stages： Data Collection， Data Annotation， Model Inference， and Model Iteration. To provide a holistic view of the field， we categorize and analyze the overarching strategies within each stage： In the Data Collection phase， the survey reviews literature aimed at circumventing costly physical data acquisition. We analyze data synthesis paradigms and data transfer mechanisms that reuse existing knowledge for new environments. In the Data Annotation phase， we evaluate methodologies designed to eliminate the reliance on exhaustive human-in-the-loop labeling. This encompasses weakly supervised learning and self-supervised learning frameworks. For Model Inference， the methodology categorizes hardware and algorithmic interventions into model lightweighting and inference acceleration. The review synthesizes approaches like knowledge distillation， efficient attention mechanisms， linear sequence architectures， and dynamic inference strategies. Finally， in the Model Iteration phase， we examine frameworks that mitigate the massive carbon footprint of repetitive retraining. The literature is organized into continual learning strategies that prevent catastrophic forgetting and parameter-efficient fine-tuning methods that adapt models with minimal parameter updates. The synthesis of current technological frameworks reveals a profound paradigm shift across all evaluated dimensions of the visual AI lifecycle， transitioning from resource-heavy processes to highly optimized， sustainable operations. Energy-Efficient Data Collection： The literature indicates a decisive shift from physical data collection toward virtual synthesis and cross-domain transfer. Generative Adversarial Networks， Diffusion Models， and Large Language Models are now highly capable of synthesizing high-fidelity， varied data. Furthermore， domain adaptation and open-vocabulary learning techniques enable models to effectively reuse source-domain knowledge， facilitating robust deployment in unknown target environments with minimal to zero new data acquisition. Annotation-Efficient Paradigms： To alleviate the immense human capital required for pixel-level annotations， the field is rapidly adopting self-driven learning signals. Weakly supervised methods successfully derive supervisory cues from coarse， image-level labels or pseudo-labels. More prominently， self-supervised strategies， particularly contrastive learning， Masked Image Modeling， and cross-modal alignment， extract intrinsic structural representations directly from massive unlabeled datasets. These methods effectively bypass the manual annotation bottleneck while yielding powerful， generalized foundation models. Low-Carbon Model Inference： Addressing the high-frequency energy costs of model deployment， current network lightweighting techniques exhibit remarkable efficacy. Knowledge distillation successfully transfers representational power from massive teacher models to compact student networks， shifting the computational burden away from the deployment phase. To break the quadratic complexity bottleneck of standard Transformers， researchers have developed linear attention mechanisms and linear sequence architectures. On the acceleration front， techniques like single-step sampling for diffusion models， Key-Value cache optimization， and dynamic routing architectures ensure models execute complex tasks with minimal computational overhead. Sustainable Model Iteration： In dynamic environments， retraining massive models from scratch for every concept drift is computationally prohibitive. The survey highlights continual learning strategies—including parameter regularization， data replay， and architectural increments—which successfully prevent catastrophic forgetting， allowing models to accumulate new knowledge continuously. Concurrently， PEFT methods， notably Adapters， Low-Rank Adaptation， and Prompt Tuning， permit adaptation to novel downstream tasks by updating only a minuscule fraction of parameters while freezing the pre-trained backbone， drastically lowering the barrier for sustained model evolution. The transition toward Green Visual AI represents a fundamental evolution of artificial intelligence from a resource-intensive discipline to a sustainable， eco-friendly ecosystem. This review demonstrates that achieving a synergistic balance between technological performance and energy efficiency is feasible through targeted， full-lifecycle optimizations. However， significant challenges remain. Future research trajectories may emphasize the integration of explicit physical constraints into generative models to ensure real-world consistency， the development of unified self-supervised frameworks for heterogeneous multi-modal data， and the deepening of hardware-software co-design. Ultimately， embedding energy efficiency into the foundational design principles of computer vision is imperative to ensure that the next generation of visual AI sustainably empowers industrial applications while actively advancing global carbon neutrality objectives.

ObjectiveCamouflaged object detection （COD） is a significant and challenging task in computer vision， which focuses on identifying and segmenting objects that are highly similar to complex backgrounds in terms of color， texture， and shape. This technique presents important research value and wide application potential in agricultural monitoring， medical imaging， ecological protection， military reconnaissance， and other fields. However， existing camouflaged object detection methods still face several critical limitations. On the one hand， convolutional neural networks （CNN） are restricted by insufficient effective receptive fields， making it difficult to capture global context information and long-range dependencies required for distinguishing camouflaged objects. On the other hand， vision Transformers rely on self-attention mechanisms with quadratic computational complexity， resulting in huge computational overhead and memory consumption， which makes it hard to balance detection accuracy and efficiency. In addition， most mainstream methods only use single-modal RGB images as input and ignore the rich geometric and spatial prior information contained in depth maps. The existing cross-modal fusion strategies are relatively simple and cannot fully exploit the complementary information between RGB and depth modalities， leading to poor detection performance in highly camouflaged scenes. Aiming at these problems， this paper conducts an in-depth study on RGB-D camouflaged object detection methods based on multi-modal state space models， so as to achieve accurate， efficient and robust camouflaged object detection by fusing appearance information and geometric priors.MethodThis paper proposes a novel RGB-D camouflaged object detection framework based on a multi-modal state space model， named MambaCOD. First， considering the lack of real depth maps in public COD datasets， the advanced visual foundation model Depth Anything V2 is employed to generate high-quality pseudo-depth maps from raw RGB images， providing reliable geometric structure priors and constructing stable RGB-D multi-modal input pairs. Second， a parameter-shared dual-branch encoder is designed to extract hierarchical multi-scale pyramid features from RGB images and depth maps respectively， ensuring the consistency of feature extraction and reducing redundant parameters. Third， a multi-modality Mamba fusion module （M3FM） based on the state space model is proposed to achieve bidirectional reciprocal feature fusion between RGB and depth modalities. This module integrates depth-wise separable convolution， 2D selective scanning （SS2D）， and bidirectional 2D selective scanning （Bi-SS2D）， which can model long-range global dependencies with linear complexity， break through the bottlenecks of traditional CNNs and Transformers， and fully mine complementary information between modalities. Fourth， a dual-directional context mixture convolution module （DCM-Conv） based on multi-kernel asymmetric convolution is constructed for the decoder stage. By channel splitting， cascaded vertical and horizontal asymmetric depth-wise separable convolutions， and channel mixing operations， this module extracts multi-receptive-field contextual features while effectively controlling the number of parameters and computational costs. On this basis， a progressive multi-scale decoder is built to fuse adjacent-scale features layer by layer and gradually output the final camouflaged object prediction mask. Finally， a hybrid loss function combining binary cross-entropy （BCE） loss， IoU loss， and structural similarity （SSIM） loss is adopted to jointly optimize the model from pixel accuracy， global structure， and boundary integrity.ResultComprehensive experiments are conducted on three challenging and widely used COD benchmark datasets： CAMO， COD10K， and NC4K. The proposed MambaCOD is compared with 11 state-of-the-art methods， including 6 RGB-based models and 5 RGB-D-based models. Quantitative results show that MambaCOD achieves the optimal performance on most key evaluation metrics， including structural measure （Sm）， enhanced alignment measure （Em）， weighted F-measure （wFm）， and mean absolute error （MAE）. Specifically， compared with the second-best method， the proposed method reduces the mean absolute error by 21.3%， 17.4%， and 12.5% on the three datasets respectively， and achieves the best values in all major metrics. Efficiency analysis indicates that the model has only 58.5M parameters and 47.6G FLOPs， which are significantly lower than most comparison methods； compared with FSPNet， the parameter quantity is reduced by 78.6%， and compared with the Samba model， FLOPs are decreased by 4.0%， achieving an excellent balance between accuracy and efficiency. Visual effect analysis demonstrates that MambaCOD generates segmentation masks highly consistent with ground truth， accurately restores the contours and fine details of camouflaged objects， effectively distinguishes targets from highly similar backgrounds， reduces background noise and false detections， and maintains complete segmentation for irregularly shaped and highly concealed targets. Ablation experiments verify that each core component， including M3FM and DCM-Conv， contributes effectively to performance improvement. Further experiments confirm that Depth Anything V2 provides higher-quality geometric priors than traditional depth generators such as DPT， and the proposed modules still maintain effectiveness under different backbone networks.ConclusionThis paper proposes a lightweight and high-performance RGB-D camouflaged object detection framework MambaCOD based on a multi-modal state space model. By introducing high-quality pseudo-depth maps generated by Depth Anything V2， the model enriches the geometric structure information of input features and strengthens the multi-modal fusion between RGB appearance and depth geometry. The designed M3FM module realizes efficient bidirectional cross-modal fusion based on Mamba， which effectively captures long-range dependencies with linear complexity and breaks through the limitations of traditional CNNs and Transformers. The DCM-Conv module constructs a high-efficiency multi-scale decoder through asymmetric multi-kernel convolution， further improving detection accuracy while controlling computational costs. Experimental results on multiple benchmark datasets show that the proposed method outperforms existing mainstream approaches in both detection performance and computational efficiency， achieving state-of-the-art COD results. The proposed method effectively solves the problems of insufficient feature representation， low cross-modal fusion efficiency， and unbalanced precision and efficiency in traditional methods， providing a new solution for high-precision and high-efficiency camouflaged object detection in complex scenes. In the future， we will focus on enhancing the model’s robustness to noisy depth inputs， extending the framework to video camouflaged object detection， and exploring lightweight deployment on resource-constrained edge devices to expand the practical application scope of the model.

ObjectiveIndustrial safety monitoring often involves rare but high-risk events that exhibit a pronounced long-tail distribution. In real deployment scenarios， these critical hazard categories occur infrequently， are expensive to annotate at scale， and are often defined by subtle visual cues that must be interpreted together with contextual evidence from the surrounding scene. As a result， conventional vision-based safety monitoring methods， which typically rely on large annotated datasets and closed-set classification assumptions， often struggle to generalize to unseen hazard categories in practical industrial environments. Although recent vision-language models have demonstrated strong cross-modal understanding and reasoning capabilities， their direct application to industrial safety recognition still faces several fundamental challenges. First， existing models are often insufficiently sensitive to small but decisive local details， such as missing protective equipment， improper operation near machinery， or subtle environmental anomalies. Second， their few-shot adaptation ability is unstable when the support examples are limited and the target category has never appeared during training. Third， the generated reasoning text may not be well aligned with the final decision， which reduces the reliability and interpretability of the output. Finally， these models may generate fluent but weakly grounded explanations that are not fully supported by the visual evidence. To address these issues， this study investigates a few-shot industrial risk recognition framework that aims to improve both generalization to unseen hazard categories and the interpretability， factual consistency， and structural reliability of the decision process.MethodsA contextual chain-of-thought driven framework is developed for few-shot industrial safety risk recognition. The proposed method is built on three tightly coupled components. First， a dual-database data organization strategy is adopted to support different stages of learning. A core chain-of-thought training set is constructed to teach the model a structured industrial reasoning pattern through observation， analysis， and conclusion-oriented supervision， while an external few-shot example database is designed to provide task-specific contextual examples for unseen hazard categories. This separation allows the model to learn general reasoning ability from structured supervision and then apply it to new tasks through contextual adaptation. Second， the model architecture is enhanced by a hierarchical vision encoder， a semantic consistency classification head， and an active perception with iterative refinement mechanism. The hierarchical vision encoder fuses shallow， middle， and deep visual features so that both global scene semantics and fine-grained local details can be utilized during reasoning. The semantic consistency classification head predicts the final risk label from the semantic representation of the generated reasoning text rather than from an isolated parallel branch， thereby encouraging the final decision to remain structurally aligned with the explanation. The active perception mechanism allows the model to trigger local refinement when the initial global observation is insufficient， ambiguous， or affected by clutter， occlusion， or small target scale， so that difficult samples can receive additional focused inspection on critical regions. Third， a two-stage training strategy is adopted. In the first stage， structured chain-of-thought supervision is used to inject an industrial safety reasoning pattern into the model by jointly optimizing reasoning generation， final risk classification， and image-text semantic grounding. In the second stage， meta-samples composed of contextual examples and a query image are used to explicitly train in-context generalization， so that the model learns to infer a new risk concept from only a few support examples rather than relying on memorized category names. A contrastive image-grounding loss is further introduced to strengthen the factual alignment between visual evidence and generated text， reduce hallucination， and improve the faithfulness of the reasoning process.ResultsExperiments are conducted on a dedicated evaluation protocol for unseen industrial hazard recognition. The core supervised training set contains three common industrial risk categories with structured reasoning annotations， while the UH-14 benchmark is used to evaluate generalization to 14 unseen hazard categories under few-shot settings. Quantitative experiments are reported under 1-shot， 3-shot， and 5-shot settings. Under the 3-shot setting on UH-14， the proposed method achieves an F1-score of 68.56%， outperforming ChatCH-SFT by 12.81 percentage points over its 55.75% result. The F2-score improves from 57.83% to 70.81%， and recall reaches 72.40%， indicating a substantially stronger ability to reduce missed detections in safety-critical scenarios where recall is particularly important. Under the 1-shot setting， the proposed model still achieves an F1-score of 56.92%， demonstrating that the framework remains effective even when only a single support example is available for each unseen class. Under the 5-shot setting， the model reaches an F1-score of 74.49% and an F2-score of 76.27%， showing that the method can continue to benefit from additional contextual examples. Ablation experiments further verify the contribution of the major components. Removing the contextual learning stage causes a significant drop in performance， with the F1-score decreasing by 19.72 percentage points and the F2-score decreasing by 19.51 percentage points， which confirms that explicit contextual generalization training is central to the framework. Removing the chain-of-thought learning stage also leads to clear degradation， with the F1-score and F2-score dropping by 4.76 and 5.42 percentage points， respectively， indicating that structured reasoning supervision provides an important foundation for downstream few-shot transfer. Additional analyses show that the proposed design is beneficial not only for recognition accuracy but also for improving explanation consistency and strengthening the robustness of decisions on visually complex or ambiguous samples.ConclusionThe proposed framework improves few-shot recognition of unseen industrial risk categories while providing a more interpretable， structurally consistent， and visually grounded decision process. By coupling contextual learning， hierarchical visual perception， semantic consistency constraints， and conditional iterative refinement， the method is particularly suitable for industrial safety scenarios characterized by long-tail hazards， scarce annotations， strong dependence on contextual cues， and strict recall requirements. The framework does not merely improve classification performance； it also strengthens the factual reliability and logical coherence of generated explanations， which is important for human verification， expert review， and practical safety deployment in real industrial environments. In this sense， the study contributes not only a recognition model， but also a pathway toward more trustworthy multimodal reasoning for safety-critical applications. At the same time， the current study is still mainly focused on static-image scenarios. Future work may extend the method toward video-based temporal reasoning， deployment-oriented efficiency optimization， larger-scale open-category evaluation protocols， and more systematic construction of industrial reasoning data， so as to further improve practical applicability in real-world safety monitoring systems.