基于改进YOLOv4的车辆检测算法

doi:10.16180/j.cnki.issn1007-7820.2025.01.011

Abstract

Abstract:

In the process of vehicle detection in traffic monitoring, there are some problems such as vehicles shielding each other and insufficient distance target size, which leads to missing detection and false detection. To solve this problem, this study proposes a traffic vehicle detection algorithm based on YOLOv4(You Only Look Once version 4) multi-scale fusion and attention mechanism. A new feature layer is added to YOLOv4's path aggregation network for multi-scale feature fusion to improve the model's ability to extract underlying texture features. The ECA (Efficient Channel Attention) channel attention module is embedded in front of YOLO Head detection head to reasonably suppress and enhance the aggregated features. The CIoU (Complete Intersection over Union) loss function is replaced by the Soft-CIoU loss function to improve the contribution of small target vehicles to the loss function. The experimental results on the publicly available vehicle data sets UA-DETRAC and KITTI show that compared to the original YOLOv4 algorithm, the average accuracy of the proposed algorithm improves by 2.45 percentage points and 1.14 percentage points, respectively, and the detection speed reaches 41.67 frame·s^-1.The proposed algorithm performs well in detection accuracy when compared with other advanced algorithms.

Key words: vehicle detection, multi-scale feature fusion, attention mechanism, Soft-CIOU loss function, YOLOv4, deep learning, target detection, small target

CLC Number:

TP391

LAI Ying, JU Zhiyong, YE Yuxin. A Vehicle Detection Algorithm Based on Improved YOLOv4[J].Electronic Science and Technology, 2025, 38(1): 81-87.

Figures/Tables 9

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References 21

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权重因子k	AP/%
权重因子k	UADETRAC	KITTI
1	73.03	95.26
2	73.25	95.31
3	72.94	95.02
4	72.83	93.45
5	70.81	92.12

模型	AP/%	Precision/%	Recall/%
YOLOv4	72.89	83.56	69.53
YOLOv4-1	74.62	84.37	68.72
YOLOv4-2	73.74	84.12	69.01
YOLOv4-3	73.25	83.77	69.43
改进YOLOv4	75.34	84.75	69.89

模型	AP/%	Precision/%	Recall/%
YOLOv4	94.90	96.10	89.73
YOLOv4-1	95.97	96.55	91.07
YOLOv4-2	95.43	96.43	91.02
YOLOv4-3	95.31	96.35	90.51
改进YOLOv4	96.31	96.89	91.01

模型	AP/%		FPS /frame·s^-1
模型	UADETRAC	KITTI	FPS /frame·s^-1
Faster RCNN	71.17	89.02	8.14
SSD	67.75	85.67	30.43
YOLOV3	40.13	72.32	52.06
YOLOV4	72.89	94.90	55.51
改进YOLOv4	75.34	96.31	41.67

A Vehicle Detection Algorithm Based on Improved YOLOv4

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