Road traffic target detection based on improved YOLOv5 algorithm

Junchao Huang; Chengming Qi

doi:10.1117/12.3039556

4 September 2024 Road traffic target detection based on improved YOLOv5 algorithm

Junchao Huang, Chengming Qi

Author Affiliations +

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 1325935 (2024) https://doi.org/10.1117/12.3039556
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

As economic development and social progress bring convenience, they also bring a large number of problems. In the field of transport, the increase in the number of vehicles travelling on the road has led to an increase in traffic problems. With the introduction of intelligent transport, the problem of road traffic has been alleviated. in the field of traffic how to quickly and accurately identify the surrounding information to assist the driver in make decisions is crucial. In this paper, an improved YOLO algorithm model is proposed for solving related problems. In order to reduce the number of parameters in the model and accelerate the inference speed of the model, it is proposed to use Faster-Block as the backbone network, and further reduce the number of parameters by using GSConv convolution in the Neck part, and finally, the model is further compressed by using a pruning algorithm that learns convolution efficiently in a network thinning. And the improved algorithm was validated in the VOC dataset as well as the CCTSDB dataset for experiments, pruning the size of the model as well as the number of parameters while keeping the model mAP/0.5 without too much change, which reduces the inference time and achieves good results, and is more convenient to be used for deployment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junchao Huang and Chengming Qi "Road traffic target detection based on improved YOLOv5 algorithm", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 1325935 (4 September 2024); https://doi.org/10.1117/12.3039556

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KEYWORDS

Data modeling

Detection and tracking algorithms

Roads

Convolution

Target detection

Ablation

Feature extraction

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