An improvement of vehicle and passerby recognition based on YOLO-V3 algorithm

Tianyang Ling; Shuo Tian; Songyuheng Gao; Zhixue Xing; Jianshao Lai; Zhenzhai Li

doi:10.1117/12.2671229

28 April 2023 An improvement of vehicle and passerby recognition based on YOLO-V3 algorithm

Tianyang Ling, Shuo Tian, Songyuheng Gao, Zhixue Xing, Jianshao Lai, Zhenzhai Li

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126102U (2023) https://doi.org/10.1117/12.2671229
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

In order to reduce the incidence of traffic accidents, the use of computer vision to identify vehicles and passers-by in the process of driving can achieve the effect of assisting driving. This paper mainly introduces the performance improvement brought by the introduction of the SPP module in YOLO-V3 for object recognition. Model training is performed on the VOC dataset based on YOLO-V3-SPP. Finally, 300 photos were used to test the accuracy of the algorithm. The results show that the recognition accuracy of YOLO-V3-SPP for vehicles and pedestrians can reach 94.19% and 90.68%, and the accuracy of YOLO-V3 is improved by nearly ten under the same equipment. percentage point. The research on this technology can effectively reduce the probability of traffic accidents and provide reference value for the future driving safety warning field.

Citation Download Citation

Tianyang Ling, Shuo Tian, Songyuheng Gao, Zhixue Xing, Jianshao Lai, and Zhenzhai Li "An improvement of vehicle and passerby recognition based on YOLO-V3 algorithm", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126102U (28 April 2023); https://doi.org/10.1117/12.2671229

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KEYWORDS

Detection and tracking algorithms

Education and training

Target detection

Computer vision technology

Data modeling

Image fusion

Convolution

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