The vehicle color recognition based on enhanced Yolov5 neural network

Yunchao Li; Jihui Wang; Xiufang Li; Zhiqi Huang

doi:10.1117/12.2688612

27 November 2023 The vehicle color recognition based on enhanced Yolov5 neural network

Yunchao Li, Jihui Wang, Xiufang Li, Zhiqi Huang

Proceedings Volume 12767, Optoelectronic Imaging and Multimedia Technology X; 127671M (2023) https://doi.org/10.1117/12.2688612
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Color features play a unique role in vehicle recognition. The color recognition algorithms based on deep learning neural networks are studied in this paper. A color recognition experiment is carried on to some typical deep learning neural networks, the result of the experiment proves that Yolov5 has faster training speed and higher accuracy for vehicle color recognition, so Yolov5 is chosen for the color recognition. The structure of yolov5 is optimized by adding C2f module replacing C3 module and adjusting the parameters of HSV color space when it is applied to identify 8 typical vehicle colors using BIT Vehicles data set. The modified Yolov5 make the accuracy of vehicle color recognition improved effectively to the complex color vehicles and part covered vehicles comparing with original yolov5 network.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yunchao Li, Jihui Wang, Xiufang Li, and Zhiqi Huang "The vehicle color recognition based on enhanced Yolov5 neural network", Proc. SPIE 12767, Optoelectronic Imaging and Multimedia Technology X, 127671M (27 November 2023); https://doi.org/10.1117/12.2688612

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