Abstract: Target detection is a fundamental and routine task in remote sensing image processing. In this paper, we design a target detection algorithm for remote sensing images based on the YOLOX network. Firstly, ASFF is added to the feature extraction module PANet to deeply mine the fine features with inconsistent scale in target detection. Secondly, an ECA-based feature extraction module is designed with efficient channel interaction to reduce the complexity of the model while paying more attention to the positive sample feature information in the feature map. Then, to avoid the problem of gradient disappearance and weak activation effect caused by overfitting, the use of swish activation function is proposed. Finally, experiments are conducted on DOTA to verify the best mechanism and effectiveness of the improved method through qualitative analyses of the ablation experiments and quantitative comparison experiments. With the addition of ASFF and ECA mechanisms and the optimization of the swish activation function, the improved network model achieves an mAP of 74.42%, an improvement of 12.75% over the original network. Compared with the current widely used target detection algorithms Mobilenet-YOLOv4, YOLOv4, YOLOv5 and YOLOX, the proposed  algorithm achieves an improvement of 11.42%-17.84% in mAP accuracy.

Key words: photogrammetry, remote sensing, target detection, DOTA, ECA, ASFF