Aerial-photography dense small target detection algorithm based on adaptive cooperative attention mechanism

In response to the problem of a large number of targets and a high proportion of small targets in a wide field of view in drone aerial target detection tasks，a drone aerial target detection algorithm ACAM-YOLO based on adaptive collaborative attention mechanism is proposed. In the backbone network and feature enhancement network parts，the Adaptive Co-Attention Module（ACAM）is embedded，which first segments the input features along the channel direc⁃ tion，Then，spatial attention features and channel attention features are separately mined，and finally adaptively weighted into collaborative attention weights to increase the effective utilization of spatial and channel information for in⁃ put features；To improve detection accuracy while ensuring lightweight of the detection network，the backbone net⁃ work，feature enhancement network，and detection head are optimized. Firstly，a lightweight backbone network is used to significantly reduce the number of parameters，and then a high-resolution feature enhancement network is used to retain more semantic features and detailed features. Finally，the positioning accuracy is improved by using a large and dense number of anchor boxes in the large-scale detection head. Verified using the public dataset Vis⁃ Drone2019，compared with the baseline network version 6. 0 YOLOv5 object detection algorithm，ACAM-YOLO’s mAP0. 5 increased by 11. 0%，mAP0. 95 increased by 7. 8%，and model parameters decreased by 65. 5%. The experi⁃ ment proved that the ACAM-YOLO object detection network has strong practicality for detecting dense small targets in aerial photography. © 2023 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.