Small Object Detection in Drone Images Based on CDCA-YOLOv8
A new small object detection algorithm CDCA-YOLOv8 is proposed to solve the problem of multiple small object instances and severe occlusion in drone aerial images.The algorithm introduces a central attention mechanism in the backbone network,which improves feature extraction capability while reducing computational complexity.Combining the advantages of deformable convolutional networks,the convolution module is improved and a C2f module based on deformable convolution technology is designed to enhance multi-scale feature extraction.A detection head based on adaptive structural feature fusion is designed to improve the accuracy of small target detection.The experiment results show that compared with YOLOv8n,CDCA-YOLOv8 improves the mean average accuracy mAP0.5 by 4.4 percentage points on the VisDrone2019 dataset,and mAP0.5∶0.95 improves by 3.1 percentage points,which demonstrates better small object detection performance.
万方数据
维普
