Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (1): 108-116.DOI: 10.3778/j.issn.1002-8331.2205-0200

• Improvement and Application of YOLO • Previous Articles     Next Articles

Improving UAV Object Detection Algorithm for YOLOv5s

SONG Puyi, CHEN Hong, GOU Haobo   

  1. 1.School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710021, China
    2.Shaanxi Lingyun Electrical Appliance Group Co., LTD., Baoji, Shaanxi 721006, China
  • Online:2023-01-01 Published:2023-01-01



  1. 1.西安工业大学 电子信息工程学院,西安 710021
    2.陕西凌云电器集团有限公司 卫星导航研究所,陕西 宝鸡 721006

Abstract: In the field of intelligence, reconnaissance and surveillance, automatic target detection can provide accurate target location and category for reconnaissance and other tasks, and provide detailed target information for ground commanders. Based on the complex background, high resolution and target scale difference of UAV images, a modified YOLOv5s object detection algorithm is proposed. Firstly, the compression-excitation module is introduced into the YOLOv5s algorithm to improve the feature extraction ability of the network. Secondly, the double-cone feature fusion(bifrustum feature fusion, BFF) structure is introduced to improve the detection accuracy of the algorithm for smaller targets. Finally, CIoU Loss replaces GIoU Loss as the loss function of the algorithm to improve the positioning accuracy of improving the bounding box regression rate. The experimental results show that the improved YOLOv5s achieves an average mean accuracy(mAP) of 86.3%, 16.8 percentage points higher compared to the original algorithm YOLOv5s, and can still significantly improve the UAV image target detection performance in a complex background.

Key words: UAV detection, YOLOv5s, compression excitation module, CIoU Loss

摘要: 无人机在情报、侦察和监视领域,目标自动检测可为侦察等任务提供准确的目标位置及类别,为地面指挥人员提供详尽的目标信息。针对无人机图像背景复杂、分辨率高、目标尺度差异大等特点,提出一种改进YOLOv5s目标检测算法。将压缩-激励模块引入到YOLOv5s算法中,提高网络的特征提取能力;引入双锥台特征融合(bifrustum feature fusion,BFF)结构,提高算法对较小目标的检测检测精度;将CIoU Loss替换GIoU Loss作为算法的损失函数,在提高边界框回归速率的同时提高定位精度。实验结果表明,改进后的YOLOv5s取得了86.3%的平均均值精度(mAP),比原算法YOLOv5s提高了16.8个百分点,在复杂背景下仍能显著提升无人机图像目标检测性能。

关键词: 无人机检测, YOLOv5s, 压缩激励模块, CIoU Loss