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陈晓雷,杜泽龙,张学功等.畸变自适应与位置感知的360度全景图像显著目标检测网络[J].中国图象图形学报,
Chen Xiaolei,Du Zelong,Zhang Xuegong,et al.Distortion-adaptive and position-aware network for salient object detection in 360° omnidirectional image[J].Journal of Image and Graphics,
目的
2
现有360°全景图像显著目标检测方法一定程度上解决了360°全景图像投影后的几何畸变问题，但是这些方法面对复杂场景或是前景与背景对比度较低的场景时，容易受到背景干扰，导致检测效果不佳。为了同时解决几何畸变和背景干扰，本文提出了一种畸变自适应与位置感知的360°全景图像显著目标检测网络（distortion-adaptive and position-aware network，DPNet）。
方法
2
提出了两个对畸变和位置敏感的自适应检测模块：畸变自适应模块（distortion-adaptive module，DAM）和位置感知模块（position-aware module，PAM）。它们可以帮助模型根据等矩形投影的特点和具体图像来决定该关注图像的哪些区域。在此基础上，本文进一步提出了一个显著信息增强模块（salient information enhancement module，SIEM），该模块用高级特征来指导低级特征，过滤其中的非显著信息，防止背景干扰对360°显著目标检测效果的影响。
结果
2
实验在2个公开数据集（360-SOD，360-SSOD）上与最新的13种方法进行了客观指标和主观结果的比较，在8个评价指标上的综合性能优于最新的13种方法。并且本文还设置了泛化性实验，采用交叉验证的方式证明了本文模型优秀的泛化性能。
结论
2
本文所提出的360°全景图像显著目标检测模型DPNet，同时考虑了360°全景图像投影后的几何畸变问题和复杂场景下的背景干扰问题，能够有效地、完全自适应地检测显著目标。
Objective
2
Salient object detection （SOD） in the field of computer vision originates from the study of human visual attention mechanisms. Its goal is to emulate the human ability to focus on specific objects or areas in complex scenes that human eyes are most interested in. Serving as a foundational research area in computer vision， SOD holds significance for various downstream tasks， such as object tracking， semantic segmentation， person re-identification， camouflaged object detection， and image retrieval， etc. In recent years， spurred by advancements in VR and AR technologies， SOD is no longer limited to traditional 2D images， it has progressively expanded to encompass 360° omnidirectional images （or panoramic images）. The application of 360° SOD serves as a crucial preprocessing step for enhancing the efficiency of subsequent advanced visual tasks. These tasks include coding， editing， stitching， quality assessment and transmission of 360° omnidirectional images. In contrast to traditional 2D images， 360° omnidirectional images exhibit the following core differences： 360° omnidirectional images are spherical. Since there is no encoder specifically for spherical images， it is necessary to project 360° omnidirectional images into 2D images for further processing. Common projection methods include equirectangular projection （ERP）， cube-map projection （CMP）， octahedron projection （OHP）， and so on. Regardless of the projection method used， geometric distortion is inevitable. This geometric distortion severely impacts the performance of SOD， resulting in a significant decline in performance when traditional 2D SOD methods are directly applied to 360° omnidirectional images. Therefore， addressing the challenge of geometric distortion generated by 360° omnidirectional image projection is the core problem in the field of salient object detection in 360° omnidirectional image （360° SOD）. In recent years， some 360° SOD methods have tried to solve the problem of geometric distortion caused by projection， and have achieved good detection results to a certain extent. But their approaches are either limited in effectiveness or rely on artificially designed features， restricting the model’s ability to detect salient objects in 360° omnidirectional images. Meanwhile， most of the models have poor detection results when facing complex scenes or scenes with low contrast between foreground and background， which are easily interfered with by the background. To solve the above problems， this paper introduces a distortion-adaptive and position-aware network （DPNet） for 360° SOD， aiming to further solve the problem of background interference in complex scenes by considering geometric distortion of ERP image， so as to better detect salient objects in 360° omnidirectional images.
Method
2
DPNet combines vision transformer （ViT） and convolutional neural networks （CNN） to build the basic framework of the network. It uses ViT and CNN to design the encoder and constructs a combination decoder based on U-Net architecture to decode the features from the two encoders step by step， so as to combine the global coding advantages of ViT and the local coding advantages of CNN. It should be noted that compared with previous dual parallel structures， the two encoder backbones of our network are not only parallel， but the ViT backbone also plays a guiding role for the CNN backbone， i.e. the CNN backbone can complement the detail information based on the semantic features extracted by the ViT backbone. On the one hand， to solve the geometric distortion problem caused by ERP， this paper proposes two distortion-adaptive detection modules： distortion-adaptive module （DAM） and position-aware module （PAM）. DAM models geometric distortion in feature maps through channel-by-channel deformable convolution. PAM calculates spatial weights along the latitude and longitude， directing the network to adaptively focus on salient regions in the image. Specifically， the global features extracted by the ViT backbone are processed by the DAM to model the geometric distortion， and then two branches are extracted， one branch is sent to the decoder， and the other branch is sent to PAM to provide position prior information. PAM is placed in the shallow layer of CNN backbone and is responsible for fusing the position prior information with the information extracted in the shallow layer of CNN backbone to guide the subsequent feature extraction. In this way， DPNet can decide which regions of the 360° omnidirectional images should be focused on according to the characteristics of ERP and specific input images. On the other hand， to further solve the problem of background interference in complex scenes， a salient information enhancement module （SIEM） is proposed. Currently， most SOD methods use structures such as U-Net to simply aggregate feature maps at different scales， which inevitably treats a large amount of non-salient information contained in the low-level features as useful information， leading to poor detection results. To address this， SIEM uses high-level features to guide low-level features， filters non-salient information， and prevents the influence of background interference on the effectiveness of 360° SOD.
Result
2
We compared our model with 13 state-of-the-art methods on 2 public datasets， namely， 360-SOD and 360-SSOD， and the overall performance of 8 evaluation metrics was better than the latest 13 methods. In addition， the generalization experiment is also set up in this paper， and the excellent generalization performance of the model is proved by cross-validation. And then an ablation experiment is set up to verify the performance of the proposed module. Finally， a set of complexity comparison experiments prove that the proposed model DPNet achieves a good balance in terms of detection accuracy and model complexity.
Conclusion
2
The existing 360° SOD methods cannot effectively address the geometric distortion problem after projection and the background interference problem in complex scenes， so we propose a distortion-adaptive and position-aware 360° SOD network （DPNet） based on the ViT and CNN. The proposed DAM and PAM play a pivotal role in guiding the network to focus on areas requiring attention based on the distinctive characteristics of ERP and specific input images. In addition， the proposed SIEM works to guide low-level features with high-level features， effectively filtering out non-salient information present in low-level features and enhancing the salient information， which can help the model effectively deal with the background interference problem in complex scenes. Through an extensive set of experiments， we demonstrate that our method outperforms 13 state-of-the-art SOD methods， establishing its superiority in 360° SOD applications.
360°全景图像显著目标检测畸变自适应位置感知抗背景干扰
360°omnidirectional imagesalient object detectiondistortion-adaptiveposition-awareanti-background interference
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