关键词: 无人机, 深度学习, 目标检测, 机器视觉, XMB-YOLOv5s

Abstract: From the drone viewpoint， the detection of dense， small targets faces various shortcomings， such as low accuracy， false detection of certain targets， and missed detections. To address these issues， this paper proposes a drone-based small target detection technique using XMB-YOLOv5s. Firstly， a small target detection layer is adopted for more effective extraction and utilization of detail information within the image. Secondly， the structured embedding of BottleneckCSP and C3TR modules is used to update the C3 module to reduce computational complexity and improve training efficiency. Subsequently， the integration of the CBAM attention mechanism enables the network to better recognize and process features， thus enhancing image recognition accuracy. Finally， the Focal-EIoU Loss is employed to resolve the insensitivity of the CIoU Loss to small target detection. The experimental results indicate that， compared with traditional YOLOv5s algorithm， the XMB-YOLOv5s algorithm has increased P by 4.6 percentage points， R by 4.4 percentage points， mAP50 by 4.9 percentage points， mAP75 by 5.1 percentage points， mAP50-95 by 4 percentage points on the VisDrone2019 data set， providing a novel approach for small target detection in drone applications.

Key words: unmanned aerial vehicles （UAV）, deep learning, object detection, machine vision, XMB-YOLOv5s