Objective

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Remote sensing image object detection plays an important role in military security， maritime traffic supervision， intelligent monitoring， and other fields. Remote sensing images are different from natural images. Most remote sensing images are taken at altitudes ranging from several kilometers to tens of thousands of meters. Therefore， the scale of target objects in remote sensing images is large. Most of the target objects are small， such as small vehicles. The other target objects are huge， such as ships. The angles of the objects in the remote sensing images are distributed arbitrarily because of the shooting angle. Therefore， this scenario is a huge challenge for the feature extraction network in remote sensing image target detection， particularly in complex backgrounds. Given the continuous improvement in the computing power of hardware devices and the rapid development of deep learning theory， large and ultralarge object detection networks have been continuously proposed in recent years to improve detection accuracy. Although these detection networks have strong representation learning capabilities， they ignore the cost-effectiveness gained from the relationship of detection accuracy with model calculation amount and the number of parameters. Moreover， real-time detection requirements are difficult to achieve， and the number of parameters and amount of calculation are very limited in model deployment. In addition， most of the general target detection models are designed for natural field datasets. The detection effect in remote sensing image target detection is unsatisfactory， particularly for densely arranged objects. The traditional horizontal box object detection cannot achieve precise detection， such as ships in port and cars in parking lots. Aiming at the above problems， a lightweight rotating box remote sensing image object detection model （YOLO-RMV4） is designed.

Method

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In the experiment， the open-source datasets DOTA2.0， FAIR1M， and HRSC2016 are used as the basic datasets. Moreover， four common vehicles， including a ship， a plane， a small vehicle， and a large vehicle， are selected as objects. A aerial images of vehicle ship and plane （AVSP） dataset is prepared after preprocesses， such as filtering， segmentation， conversion， and relabeling， are performed. This dataset contains 19 406 images of 1 024 × 1 024 and 637 466 object instances. The AVSP data labels are divided into HBB and OBB （HBB is the horizontal box annotation， and OBB is the rotating box annotation）， where OBB is represented by eight parameters. YOLO-RMV4 is improved based on the MobileNetv3 network. Adding an efficient channel attention （ECA） mechanism module with excellent performance in the feature extraction network， appropriately expanding the network scale， adding the SPPF module after the feature extraction network， and adding the path aggregation network （PANet） result in multiscale fusion of the extracted features of the backbone network， thereby providing the network with rich and reliable target features. In the network detection head， multiscale detection technology is used to deal with target objects of different sizes. More than half of the objects in the dataset are small targets. Thus， the detection after four times of downsampling is added， resulting in 4， 8， 16， and 32 times of downsampling. Moreover， the small target loss is given a high weight. The smooth circular label is added to the angle prediction in the detection head， which converts the angle regression problem into a classification problem. Thus， the distance between the predicted angle and the real angle can be measured， and the angle periodicity problem is solved. This scenario results in a precise bounding box positioning. Moreover， the anchor size is designed according to the characteristics of the dataset. We use random cropping， flipping， mosaic technique， and other data augmentation approaches in the training.

Result

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In this study， we conduct comparative experiments， and ablation experiments are carried out on the AVSP dataset. We also conduct comparative experiments on seven mainstream lightweight network models to verify the effectiveness of the model. we used average recall （AR）， mean average precision （mAP）， parameter count， and detection speed （frames per second， FPS） as evaluation metrics. Each model’s parameters， such as mAP， AR， and FPS， are also compared. The size of YOLO-RMV4 （5.3 M） is only 1/8 of that of RYOLOv5l （45.3 M）. Compared with the mAP and AR of RYOLOv5l， those of YOLO-RMV4 are increased by 1.2% and 1.6%， respectively. Moreover， the mAP and AR of YOLO-RMV4 are much higher than those of other lightweight network models （EfficientNet and ShuffleNet）. We also compress and prune YOLO-RMV4 to obtain YOLO-RMV4S， whose size is only 4.5 M. YOLO-RMV4S is also better than common lightweight network models in terms of detection precision and recall. Ablation experiments were also conducted on each improved module to verify the improvement degree of model performance by different modules. The mAP increases by 8.4% after the addition of PANet. PANet fuses the features of different layers. This phenomenon largely makes up for the defect of the limited feature extraction capability of the lightweight network. After the rotation detection head is added， the mAP increases by 16.8%， greatly increasing the detection performance of the model. After the ECA module is added， the mAP increases by 1.6%. The ECA module can accurately stimulate the backbone feature extraction network to utilize the limited capacity and the limited amount of parameters and learn the feature information of the target object. After the addition of four times of downsampling， the mAP increases by 3.0%. The addition of four times of downsampling greatly enhances the performance of small target objects. One of the modules is also eliminated based on YOLO-RMV4. The performance degradation degree of the model is compared to reflect the unique role of each module in the model. Finally， the detection accuracy of each category is analyzed. The mAP and AR of the plane are the highest. Those of the ship and the large vehicle are the second， whereas those of the small vehicle are the lowest.

Conclusion

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YOLO-RMV4 is supplemented by multiscale fusion and rotating box detection under the lightweight network structure. Thus， the model can achieve real-time inference and high-precision detection under extremely limited parameters， thereby making it very cost-effective.