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Improved YOLOv5s Algorithm for Small Target Engineering Vehicles Detection with Fixed-Point Cameras

Shiliang XU1, Minquan LAI2, Yu LEI1, Jizhong LIU2()
Jiangxi Natural Resources Development Center, Nanchang Jiangxi 330025, China
School of Advanced Manufactoring, Nanchang University, Nanchang Jiangxi 330031, China
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Abstract

In order to effectively reduce and prevent the damage to the natural environment caused by illegal land reclamation and mineral excavation, an ETS-YOLO small target monitoring and recognition algorithm for the detection of various types of engineering vehicles in complex environments is proposed using cameras deployed to high towers. Firstly, the EfficientViT network is used to replace the backbone feature extraction network of YOLOv5s in order to improve the attention diversity and significantly reduce the number of model parameters. Secondly, a small target detection layer is added to enhance the network’s extraction of shallow semantic information to improve the performance of small target detection. Finally, the original NMS function is replaced with the soft non-maximal suppression algorithm (soft-NMS) to effectively recognize occluded and overlapped targets. The experimental results show that the improved model has a mean average precision (mAP) of 93.3%, a parameter count of 5.90 M, and a detection speed of 52 f/s. Compared with the YOLOv5s model, the mAP is improved by 2.6% and the parameter count is decreased by 16.1%.

Keywords

target detectionengineering vehiclesEfficientViTimproved YOLOv5s modelsoft-NMS
CLC number: TP391 Document code: A Article ID: 2096-7675(2025)01-0099-08

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Journal of Xinjiang University(Natural Science Edition in Chinese and English)
Volume 42 Issue 1,
January 2025
Pages 99-106
DOI: 10.13568/j.cnki.651094.651316.2024.04.28.0001
Cite this article:
XU S, LAI M, LEI Y, et al. Improved YOLOv5s Algorithm for Small Target Engineering Vehicles Detection with Fixed-Point Cameras. Journal of Xinjiang University(Natural Science Edition in Chinese and English), 2025, 42(1): 99-106. https://doi.org/10.13568/j.cnki.651094.651316.2024.04.28.0001
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