Design of Intelligent Traffic Sign Image Recognition System Based on Machine Learning Algorithms

Authors

  • Jing Wang College of Intelligent Manufacturing and Automotive, Chongqing Vocational College of Transportation, Jiangjin 402247, Chongqing, China

Abstract

While automobiles offer a convenient mode of transport, autonomous driving and unmanned driving have also begun to enter the commercial stage, but they have also given rise to an increasing number of vehicle safety issues. The image recognition of traffic signs (TS) is crucial for road safety. Therefore, research on automatic recognition of TS images is essential. However, changes in weather, shadows, and light intensity can easily affect the recognition of TS, which poses significant safety risks to autonomous driving. In this paper, the function and problems of TS detection method were studied by analyzing the methods of TS identification and detection; also, corresponding system design analysis was conducted based on machine learning. The purpose of this study is to develop a high-precision and real-time TS detection system based on the interference problems
in complex environments. The relevant experimental analysis of the intelligent recognition system was carried out. The analysis showed that the recognition accuracy and anti-interference performance of TS image recognition system based on a machine learning algorithm were higher than those of traditional image recognition systems; the recognition accuracy was improved by 6.8%, and the anti-interference ability was improved by 0.24. These results suggest that machine learning algorithms can definitely improve the performance of TS image recognition systems.

Keywords: Traffic signs, image recognition systems, machine learning, traffic sign detection

Cite As

J. Wang, "Design of Intelligent Traffic Sign Image Recognition System Based on Machine Learning
Algorithms", Engineering Intelligent Systems, vol. 32 no. 5, pp. 457-464, 2024.





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Published

2024-09-01

Issue

Vol. 32 No. 5 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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