[1]HE Yuhao,YI Mingfa,ZHOU Xiancun,et al.UAV image small-target detection based on improved Yolov5[J].CAAI Transactions on Intelligent Systems,2024,19(3):635-645.[doi:10.11992/tis.202210032]
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UAV image small-target detection based on improved Yolov5

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 635-645 Column: 学术论文—机器人 Public date: 2024-05-05
Title:
UAV image small-target detection based on improved Yolov5
Author(s):
HE Yuhao1;  YI Mingfa1;  ZHOU Xiancun2;  WANG Guanling1
1. College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China;
2. The Department of Electronic and Information Engineering, West Anhui University, Lu’an 237012, China
Keywords:
image processing; GhostConv convolution module; bidirectional feature pyramid network; convolutional block attention module; soft bidirectional feature pyramid network; lightweight model; small-target detection; VisDrone dataset
CLC:
TP391.4；V19
DOI:
10.11992/tis.202210032
Abstract:
The detection speed and accuracy of small targets captured by UAV aerial photography cannot be considered at the same time. To address this problem, a new algorithm based on the Yolov5 algorithm, namely Yolov5_GBCS, is proposed for small-target detection of UAV-captured images. In the new algorithm, an additional detector head is added to enhance the feature fusion effect of small targets. In the backbone network, the GhostConv convolution module and GhostBottleneckC3 module are used to replace some original Conv modules and C3 modules for extracting rich and redundant features, respectively, to improve the model efficiency. The weighted bidirectional feature pyramid network structure is introduced to enhance the detection accuracy of small targets. The lightweight convolutional block attention module is introduced into the backbone and neck networks to focus on important features and suppress unnecessary features to boost the ability of small-target feature expression. The Soft-NMS algorithm is used to replace the NMS for reducing the miss detection rate of small targets in dense scenes. Experimental results on the VisDrone 2019 dataset show that the Yolov5_GBCS algorithm integrating all improved methods enhances the detection accuracy and effectively improves the detection speed. The mAP of the subject model has been increased from 38.5% to 43.2%, and the detection speed from 53 f/s to 59 f/s. Therefore, the Yolov5_GBCS algorithm can effectively recognize small targets in the image captured by UAV aerial photography.
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