广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (03): 71-80.doi: 10.12052/gdutxb.230044

• 计算机科学与技术 • 上一篇    下一篇

基于YOLOv5的轻量化无人机航拍小目标检测算法

李雪森1, 谭北海2, 余荣1, 薛先斌1   

  1. 1. 广东工业大学 自动化学院, 广东 广州 510006;
    2. 广东工业大学 集成电路学院, 广东 广州 510006
  • 收稿日期:2023-03-04 出版日期:2024-05-25 发布日期:2024-06-14
  • 通信作者: 谭北海(1980-),男,副教授,博士,硕士生导师,主要研究方向为AI算法及芯片设计、人工智能、深度学习,E-mail:bhtan@gdut.edu.cn
  • 作者简介:李雪森(1997-),男,硕士研究生,主要研究方向为深度学习,E-mail:18325945913@163.com
  • 基金资助:
    国家自然科学基金资助项目(61971148)；国家自然科学基金资助项目(U22A2054)；广东省基础与应用基础研究基金联合基金重点项目(2019B1515120036)；广西自然科学基金重点项目(2018GXNSFDA281013)

Small Target Detection Algorithm for Lightweight UAV Aerial Photography Based on YOLOv5

Li Xue-sen1, Tan Bei-hai2, Yu Rong1, Xue Xian-bin1   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-03-04 Online:2024-05-25 Published:2024-06-14

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摘要： 针对无人机航拍视角下图像目标特征尺寸小且存在背景复杂、分布密集的问题，提出了一种基于YOLOv5的轻量化无人机航拍小目标检测改进算法GA-YOLO。该算法改进了Mosaic数据增强方法和网络整体结构，并增加了微小物体检测头，同时设计了轻量化的全局注意力模块和并行结构的空间通道注意力机制模块，提高了网络的全局特征提取能力和训练过程中卷积通道之间的竞争和合作关系。以4.0版本的YOLOv5s为基准，在公开无人机航拍数据集VisDrone2019-DET上实验，结果表明，改进后的模型相较于原模型，参数量下降了48%，计算量下降了26%，而mAP@0.5提高了4.9个百分点，mAP@0.5:0.95提高了3.3个百分点，有效地提高了无人机空中视角下对密集型小目标的检测能力。

关键词: 无人机航拍, YOLOv5s, 小目标检测, 数据增强, 注意力机制

Abstract: A lightweight unmanned aerial vehicle (UAV) aerial photography small target detection algorithm GA-YOLO based on YOLOv5 is proposed to address the problem of small target feature size, complex background, and dense distribution in images from the perspective of UAV aerial photography. This algorithm improves the Mosaic data augmentation method and overall network structure, and adds a small object detection head. At the same time, a lightweight global attention module and a parallel spatial channel attention mechanism module are designed to enhance the network's global feature extraction ability and the competition and cooperation between convolutional channels during the training process. Based on the 4.0 version of YOLOv5s, experiments were conducted on the publicly available drone aerial photography dataset VisDrone2019-DET. The results showed that the improved model reduced the number of parameters by 48% and the computational complexity by 26% compared to the original model, and mAP@0.5 improved by 4.9 percentage points, mAP@0.5 0.95 increased by 3.3 percentage points, effectively enhancing the detection capability of unmanned aerial vehicles for dense small targets from an aerial perspective.

Key words: UAV aerial photography, YOLOv5s, small target detection, data enhancement, attention mechanism

中图分类号: 

  • TP391.41
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