Vehicle Target Detection Algorithm Based on Improved YOLOv7-tiny

doi:10.12382/bgxb.2024.0393

Abstract

Abstract:

At present,the traffic management relying on manpower is characterized by inaccurate statistics and delayed feedback.A vehicle detection algorithm based on the improved YOLOv7-tiny algorithm suitable for deploying on edge terminal devices is proposed to better protect people’s lives and property.A deep powerful residual (DP_Res)convolutional block isconstructed to perform the lightweight improvements on the efficient layer aggregation network-tiny (ELAN-T) module of backbone network.By reducing branches,the lightweight improvement on the ELAN-T module of the feature fusion network is made to reduce the number of parameters and computational load of the network,and the structure of the feature fusion network is reconstructed;The efficient channel attention mechanism and the EIOU bounding box loss function are introduced to improve the accuracy of the algorithm.The experiment is conducted on the preprocessed UA-DETRAC dataset,and the parameters of the improved algorithm are reduced by 15.1% compared to those of the original YOLOv7-tiny,with a reduction in computation of 5.3% and an increase in mAP@0.5 of 5.3 percentage points.The experimental results show that the improved algorithm not only achieves lightweight,but also improves the detection accuracy,making it suitable for deployment on edge terminal devices to complete the task of detecting vehicles on the road.

Key words: vehicle detection, YOLOv7-tiny algorithm, deep powerful residual convolutional block, efficient layer aggregation network-tiny module

CLC Number:

TP391.4

ZHAO Haili, XU Xiuchang, PAN Yuhang. Vehicle Target Detection Algorithm Based on Improved YOLOv7-tiny[J]. Acta Armamentarii, 2025, 46(4): 240393-.

Figures/Tables 13

Fig.1 Network structure of YOLOv7-tiny algorithm

Fig.2 Network structure of improved YOLOv7-tiny algorithm

Fig.3 Structure of DP_Res convolutional block

Fig.4 Structure of QLA-ELAN module

Fig.5 Structure of QLB-ELAN module

Fig.6 Brief process of improved feature fusion network

Fig.7 Structure of ECA module

Fig.8 Schematic diagram of EIOU

Table 1 Comparison of ablation experimental results

序号	输入图片尺寸	改进主干网络	改进特征融合网络	ECA	EIOU	参数量/10⁶	计算量/10⁹	mAP@0.5/%
1	3×640×640					6.02	13.2	72.1
2	3×640×640	√				5.26	11.8	71.3
3	3×640×640	√	√			5.11	12.5	73.3
4	3×640×640	√	√	√		5.11	12.5	75.5
5	3×640×640	√	√	√	√	5.11	12.5	77.4

Fig.9 Comparison of loss curves

Table 2 Comparison of experimental results of different algorithms

算法	参数量/10⁶	计算量/10⁹	mAP@ 0.5/%	帧率
YOLOv7-tiny算法	6.02	13.2	72.1	149.3
YOLOv3-tiny算法	8.68	13.0	65.7	294.1
YOLOv5s算法	7.03	16.0	74.1	166.7
YOLOv7-MobileNetv3算法	4.17	7.0	72.3	82.6
YOLOv7-GhostNet算法	6.35	9.9	73.4	75.2
本文算法	5.11	12.5	77.4	144.9

Fig.10 Comparison of detection effects of the algorithms

Fig.11 The results of the improved algorithm’s board connected inference

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