Vehicle Detection Method Based on Multi Scale Feature Fusion

Authors

Yin Wang, College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China;Follow
Feixiang Wang, College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China;Follow
Qianlai Sun, College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China;

Abstract

Abstract: Vehicle detection is the important research content and hotspot in the intelligent transportation. Aiming at the low detection accuracy and poor small-scale recognition effect of the traditional vehicle detection algorithm, an improved detection method based on YOLOv4(you only look once v4) is proposed to improve the detection performance of small target vehicles in traffic scenes. By redesigning the YOLOv4 network, the MobileNetv2 deep separable convolution module is used to replace the traditional convolution, and the convolutional block attention module (CBAM) attention module is integrated into the feature extraction network to ensure the detection accuracy of the model and reduce the model parameters. The deep and shallow semantic information of the four scale feature maps is fused by using PANet-D feature fusion to enhance the detection ability of small objects. By using Focal loss to optimize the classification loss function, the convergence speed of the network model is accelerated. The experimental results show that the recognition accuracy of the improved network reaches 96.55%, and the size of the network model is 92.49 M lower, and the detection speed is 17% higher than those of the original YOLOv4 network, which fully proves the feasibility of the algorithm.

Recommended Citation

Wang, Yin; Wang, Feixiang; and Sun, Qianlai (2022) "Vehicle Detection Method Based on Multi Scale Feature Fusion," Journal of System Simulation: Vol. 34: Iss. 6, Article 5.
DOI: 10.16182/j.issn1004731x.joss.21-0907
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss6/5

First Page

1219

Revised Date

2021-10-16

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0907

Last Page

1229

CLC

TP311

Recommended Citation

Yin Wang, Feixiang Wang, Qianlai Sun. Vehicle Detection Method Based on Multi Scale Feature Fusion[J]. Journal of System Simulation, 2022, 34(6): 1219-1229.

Corresponding Author

Feixiang Wang,2601741160@qq.com

DOI

10.16182/j.issn1004731x.joss.21-0907