A remote sensing image rotation object detection approach for real-time environmental monitoring

For the problems of resources and environment faced in sustainable development, the use of remote sensing image object detection technology is an effective means to detect and analyze this major problem. Aiming at the problems of low accuracy and slow detection speed of existing remote sensing image rotation object detection algorithms implemented based on Transformer, a remote sensing image rotation object detection method based on dynamic position information Transformer is proposed. Firstly, to improve the detection accuracy, the cross-attention operation of the decoder is improved, and the obtained results are iteratively updated with the position information of the object queries and used as the initial object queries for the next decoder; secondly, to improve the robustness of the network for remote sensing image object detection, the data processing method of image pyramid is designed; Finally, a rotating IoU matching loss function more suitable for oriented object detection is introduced to improve the accuracy of matching the predicted boxed to the true boxed. The detection algorithm proposed in this paper is experimentally verified on DOTA and SSDD datasets, and the average detection accuracy is 73.70% and 90.3%, respectively, which effectively improves the average detection accuracy of Transformer-based rotating object detection algorithm in aerial remote sensing images and has better real-time detection performance.

Automated summary

This paper proposes a remote sensing image rotation object detection method based on dynamic position information Transformer to address the problems of low accuracy and slow detection speed in existing algorithms. The method improves detection accuracy by enhancing the cross-attention operation of the decoder and iteratively updating the position information of object queries. It also improves the network's robustness for remote sensing image object detection using an image pyramid data processing method and introduces a rotating IoU matching loss function for oriented object detection to improve the accuracy of matching predicted boxes to true boxes. Experimental results on DOTA and SSDD datasets show that the proposed algorithm achieves an average detection accuracy of 73.70% and 90.3%, respectively, effectively improving the average detection accuracy of Transformer-based rotating object detection algorithms in aerial remote sensing images and providing better real-time detection performance.