基于YOLOv10-vehicle算法的复杂天气情况下车辆目标检测方法

doi:10.13229/j.cnki.jdxbgxb.20250016

Abstract

Abstract:

In the face of complex weather conditions such as cloudy days， rainy days， and nights， vehicle target detection is affected by factors such as lighting， rain， snow， and dust. As a result， problems like false detections and missed detections occur. To address these issues， a YOLOv10-vehicle target detection algorithm is proposed. Firstly， a new attention mechanism module named WT-PSA is designed to improve the model's attention to vehicle targets under complex weather. Secondly， the SPPF module is improved by introducing the average pooling operation to address the problem of insufficient feature information extraction caused by the max pooling operation. Then， an improved C2f-OD module is put forward to enhance the ability of the backbone network to extract image feature information. Finally， the model's loss function is replaced with Focal EIoU to accelerate the convergence speed and reduce the loss value. Comparative experiments are conducted on the vehicle dataset UA-DETRAC. The mean average precision （mAP@0.5） of the improved algorithm is increased by 5.1% compared with that of the original algorithm， demonstrating the superiority of the YOLOv10-vehicle algorithm in vehicle detection under complex and severe weather conditions. Meanwhile， experiments are also carried out on the VOC public dataset. The detection accuracy of the YOLOv10-vehicle algorithm in detecting vehicle targets is improved by 2.8%， which verifies the generalization ability of the improved algorithm in this paper.

Key words: vehicle target detection, YOLOv10n, C2f module, severe weather

CLC Number:

TP391.4

Hong DU,Chen-yu GU,Xiao-zheng ZHANG,Gao-tian LIU,Xing-xin LI,Zhong-lin YANG. Vehicle target detection method based on the YOLOv10-vehicle algorithm under complex weather conditions[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(10): 3309-3318.

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环境项	环境规格
CPU	Intel（R） Core（TM） i5-13600KF
内存	32 GB
显卡	NVIDIA RTX 4070
操作系统	Windows 11
编程语言	Python 3.9.19
深度学习框架	Pytorch 2.4.0
集成开发环境	Pycharm 社区版
CUDA	12.1
CUDNN	9.0.0

超参数	系数值
初始学习率η₀	0.01
循环学习率η	0.01
动量β	0.937
批次大小	16
图片尺寸/像素	640×640
训练轮次	100
预热学习轮数	3.0
预热训练动量	0.8
IoU训练阈值	0.7

实验序号	WT-PSA	C2f-OD	SPPF_A	Focal EIoU	Params/10⁶	FLOPs	mAP 0.5%
1	×	×	×	×	2.70	8.3	82.7
2	√	×	×	×	3.33	8.8	83.9
3	√	√	×	×	3.70	9.0	86.5
4	√	√	√	×	3.75	9.1	86.9
5	√	√	√	√	3.75	9.1	87.8

Model	Params/10⁶	GFLOPS	mAP 0.5%	mAP0.5%~0.95%
Faster-RCNN	36.5	23.3	93.8	60.4
YOLOv7-tiny	6.01	13.2	82.9	54.7
YOLOv8n	3.01	8.2	78.7	50.0
YOLOv10n	2.70	8.3	82.7	57.6
YOLOv10s	8.04	24.6	86.7	59.2
YOLOv11n	2.58	6.3	78.3	50.2
Ours	3.75	9.1	87.8	60.2

Model	mAP@0.5%	mAP@0.5-Car%
YLOLv10n	37.7	52.4
YOLOv10-vehicle	37.3	55.2

Vehicle target detection method based on the YOLOv10-vehicle algorithm under complex weather conditions

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