Improved YOLOX Remote Sensing Image Object Detection Algorithm

Abstract

Remote sensing image object detection is one of the core tasks of remote sensing image processing. In recent years, with the development of deep learning, great progress has been made in object detection in remote sensing. However, the problems of dense small targets, complex backgrounds and poor target positioning accuracy in remote sensing images make the detection of remote sensing targets still difficult. In order to solve these problems, this research proposes a remote sensing image object detection algorithm based on improved YOLOX-S. Firstly, the Efficient Channel Attention (ECA) module is introduced to improve the network's ability to extract features in the image and suppress useless information such as background; Secondly, the loss function is optimized to improve the regression accuracy of the target bounding box. We evaluate the effectiveness of our algorithm on the NWPU VHR-10 remote sensing image dataset, the experimental results show that the detection accuracy of the algorithm can reach 95.5%, without increasing the amount of parameters. It is significantly improved compared with that of the original YOLOX-S network, and the detection performance is much better than that of some other mainstream remote sensing image detection methods. Besides, our method also shows good generalization detection performance in experiments on aircraft images in the RSOD dataset.