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

Li Xue-sen; Tan Bei-hai; Yu Rong; Xue Xian-bin

doi:10.12052/gdutxb.230044

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

更新时间：2026-05-27

- Small Target Detection Algorithm for Lightweight UAV Aerial Photography Based on YOLOv5
- Vol. 41, Issue 3, Pages: 71-80(2024)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230044
  CLC： TP391.41
- Online First：25 May 2024，
  
  Published：2024
- 稿件说明：
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李雪森, 谭北海, 余荣, 薛先斌. 基于YOLOv5的轻量化无人机航拍小目标检测算法[J]. 广东工业大学学报, 2024, 41(3): 71-80.

Li Xue-sen, Tan Bei-hai, Yu Rong, et al. Small Target Detection Algorithm for Lightweight UAV Aerial Photography Based on YOLOv5[J]. 2024, 41(3): 71-80.
李雪森, 谭北海, 余荣, 薛先斌. 基于YOLOv5的轻量化无人机航拍小目标检测算法[J]. 广东工业大学学报, 2024, 41(3): 71-80. DOI： 10.12052/gdutxb.230044.

Li Xue-sen, Tan Bei-hai, Yu Rong, et al. Small Target Detection Algorithm for Lightweight UAV Aerial Photography Based on YOLOv5[J]. 2024, 41(3): 71-80. DOI： 10.12052/gdutxb.230044.

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。

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.

关键词

Keywords

references

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