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Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (13): 327944-327944.doi: 10.7527/S1000-6893.2022.27944

• Electronics and Electrical Engineering and Control • Previous Articles    

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

Zihao LI, Zhengping WANG, Yuntao HE()   

  1. School of Astronautics,Beijing Institute of Technology,Beijing 100081,China
  • Received:2022-08-24 Revised:2022-09-05 Accepted:2022-10-20 Online:2022-10-28 Published:2022-10-26
  • Contact: Yuntao HE E-mail:bithyt@bit.edu.cn
  • Supported by:
    Aeronautical Science Foundation of China(2020Z005072001)

Abstract:

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 direction, 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 input features; To improve detection accuracy while ensuring lightweight of the detection network, the backbone network, 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 VisDrone2019, 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 experiment proved that the ACAM-YOLO object detection network has strong practicality for detecting dense small targets in aerial photography.

Key words: computer vision, small object detection, YOLOv5, attention mechanisms, drone

CLC Number: