Lightweight aerial image object detection algorithm based on improved YOLOv5s

YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple series according to the control of network depth and width. To realize the deployment of mobile devices or embedded devices, the paper proposes a lightweight aerial image object detection algorithm (LAI-YOL...

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Bibliographic Details
Published in:Scientific reports Vol. 13; no. 1; pp. 7817 - 10
Main Authors: Deng, Lixia, Bi, Lingyun, Li, Hongquan, Chen, Haonan, Duan, Xuehu, Lou, Haitong, Zhang, Hongyu, Bi, Jingxue, Liu, Haiying
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 15.05.2023
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/117
639/705/794
Accuracy
Algorithms
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple series according to the control of network depth and width. To realize the deployment of mobile devices or embedded devices, the paper proposes a lightweight aerial image object detection algorithm (LAI-YOLOv5s) based on the improvement of YOLOv5s with a relatively small amount of calculation and parameter and relatively fast reasoning speed. Firstly, to better detect small objects, the paper replaces the minimum detection head with the maximum detection head and proposes a new feature fusion method, DFM-CPFN(Deep Feature Map Cross Path Fusion Network), to enrich the semantic information of deep features. Secondly, the paper designs a new module based on VoVNet to improve the feature extraction ability of the backbone network. Finally, based on the idea of ShuffleNetV2, the paper makes the network more lightweight without affecting detection accuracy. Based on the VisDrone2019 dataset, the detection accuracy of LAI-YOLOv5s on the mAP@0.5 index is 8.3% higher than that of the original algorithm. Compared with other series of YOLOv5 and YOLOv3 algorithms, LAI-YOLOv5s has the advantages of low computational cost and high detection accuracy.
Bibliography:ObjectType-Article-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-34892-4