Object Detection in Remote Sensing Images based on YOLOX-Tiny Biased Feature Fusion Network
Remote sensing target detection is of great significance in fields such as environmental monitoring and circuit inspection.However,there are challenges in remote sensing images,such as large differences in target scale,a large number of small targets,high inter class similarity and intra class diversity,which lead to low de-tection accuracy.To solve the above problems,a remote sensing target detection model based on YOLOX-Ti-ny is proposed.Firstly,by improving the multi-scale feature fusion network to fully utilize shallow detail infor-mation and deep semantic information,the detection ability for small targets is enhanced;Secondly,deformable convolution is introduced at the prediction end to improve the robustness of the model to targets of different scales and shapes;Finally,the SIoU loss function is used to move the prediction box in the correct direction,further improving the positioning accuracy of the model.Experiments are conducted on remote sensing datasets DIOR and RSOD,and the experimental results show that without increasing the number of parameters,the im-proved model achieves a detection accuracy of 73.68% and 97.12%,respectively,which is high compared to some other state-of-the-art models,with a high recognition rate of overlapping targets and good real-time per-formance.
Small target detectionRemote sensing imagesYOLOXDeformable convolution
NETL
NSTL
万方数据
