Object detection of optical remote sensing images is the process of providing a given optical remote sensing image dataset with object positioning frame, object category, and confidence by model processing, and it is an important task in remote sensing image processing and has practical significance in both civil and military fields. In the civil field, it can be employed to analyze the situations of airport flights and ships in ports and thus facilitate timely adjustment and avoid congestion. In the military field, enemies' military deployment is analyzed by the photographed images, and feasible plans are made to ensure successful military operations. Therefore, object detection of remote sensing images has research significance and application prospect. Compared with the traditional detection algorithms, the detection method based on the convolutional neural network has become the mainstream object detection of remote sensing images. The method based on deep learning can yield better accuracy than the traditional object detection methods of visible light remote sensing images, and it is unnecessary to manually design rules, which has a relatively unified standard and enhances the model robustness. However, there are still many defects in introducing the object detection model dealing with natural images directly into remote sensing tasks. Starting from the oriented object detection difficulties of remote sensing, we design an oriented object detection algorithm for optical remote sensing images to improve the feature extraction and feature recognition ability of multi-scale and multi-directional remote sensing small targets in complex backgrounds.

Aiming at the poor performance of general algorithms for remote sensing oriented object detection, we propose an oriented object detection model based on SWA training strategy and feature recombination. The model is optimized based on the Rotated RPN algorithm. On the one hand, the feature recombination mechanism is introduced to make the model focus on effective features, which can reduce unnecessary computing resources and improve the model accuracy. On the other hand, based on RPN, the rotating RPN is introduced, and the position and angle parameters are regressed by the midpoint offset method to generate high-quality directed candidate frames. For the required feature inconsistency between classification and regression tasks, a polarized attention detector is employed, and the training strategy is improved. Meanwhile, the model is trained by cyclic mode to alleviate the problem that the traditional training strategy will converge to the boundary region of the optimal solution.

Specifically, we conduct the following improvements based on Rotated RPN. 1) Given the problems in the object detection tasks of remote sensing images, such as a large number of small targets, a large proportion of background, and a large change in target size, the feature pyramid can not extract effective information during extracting and fusing features, which degrades detection performance. Therefore, we consider making changes in the feature pyramid to strengthen the feature extraction ability of the feature pyramid and the ability to fully fuse information of various sizes. Additionally, the reshape module is designed and integrated into the Carafe model as a deep horizontal connection of FPN. 2) To solve the problems of angle discontinuity and edge order exchange in the critical angle of the common directed box representation, we introduce the midpoint offset method to define the directed box. An adaptive attention module is designed in front of the suggested area generation module to enhance the ability of effective feature representation and further strengthen the ability of feature extraction and characterization. 3) The features required for the classification task should have the same response to different angles, which is because the focus of the classification task should be on the target itself. Thus, it should be highly responsive to the effective information inside the prediction frame, while the features required for the regression task should be sensitive to the angle change. Meanwhile, more attention should be paid to the boundary area of the target and less attention is to the information inside the prediction frame for realizing accurate angle and position prediction and reducing interference. Therefore, to avoid feature interference between different tasks and extract key features, we introduce a polarization attention module to the shared convolution layer at the front end of the dual-branch detector and adopt different response functions to distinguish the representation ability of different features. The classification head and regression head employ an activation function and an inhibition function respectively. 4) In view of the limitation that the traditional training strategy may converge to the boundary region of the optimal solution, we introduce the SWA cyclic training strategy, obtain the corresponding weights by adopting the SGD method to train more epochs, and average these results to acquire results closest to the optimal solution.

To verify the algorithm performance, we select two remote sensing oriented annotation datasets Dior-R and HRSC2016 to compare the algorithm performance. Several typical one-stage and two-stage oriented object detection models are selected and compared with this model. On the Dior-R dataset, our algorithm yields the best accuracy of 64.49%, 4.95% higher than that of the benchmark model (Table 5). On the HRSC2016 dataset, the proposed algorithm achieves the best accuracy of 90.83%, which is 11.75% higher than that of the benchmark model (Table 7). Additionally, we analyze the performance improvement after introducing the feature recombination module, focus shift method, adaptive attention module, polarized attention detector, and SWA training strategy respectively. The experimental results show that the algorithm has sound detection performance for remote sensing oriented objects in complex backgrounds.

To improve the detection performance of oriented objects in remote sensing images, we propose an oriented object detection model based on feature recombination and polarized attention. The experimental results show that the algorithm can effectively detect oriented objects in remote sensing images, and has good performance in all kinds of scenes.