A new traffic signal detection system with high robustness and real-time requirements

Junjie Tang; Yicun Li

doi:10.1117/12.3004729

20 October 2023 A new traffic signal detection system with high robustness and real-time requirements

Junjie Tang, Yicun Li

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 1291620 (2023) https://doi.org/10.1117/12.3004729
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

Automatic traffic signal detection has great significance to the development of autonomous driving technology and vehicle warning of dangerous driving behavior. Detecting traffic lights by analyzing images obtained from mobile device cameras is a feasible solution. In this paper, a new traffic signal detection system with high robustness and real-time requirements. Firstly, the HSV color segmentation method is used to segment the original map. Then, the aspect ratio information and area information of traffic lights are utilized for secondary filtering. The morphological processing method is used to filter the background in the image. Finally, intercept the crucial area to obtain the final significance segmentation image of preprocessing. Combining the salient feature preprocessing based on traditional image processing methods with the target detection model based on deep learning, the algorithm of traffic signal detection and recognition is improved.

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

Citation Download Citation

Junjie Tang and Yicun Li "A new traffic signal detection system with high robustness and real-time requirements", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 1291620 (20 October 2023); https://doi.org/10.1117/12.3004729

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KEYWORDS

Image processing

Image segmentation

RGB color model

Signal detection

Education and training

Tunable filters

Detection and tracking algorithms

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