Round Traffic Sign Detection Algorithm

Traffic signs are an important part of autonomous driving and intelligent transportation. It provides instructions for pedestrians and vehicles and is critical to road traffic safety. However, existing detection algorithms cannot achieve real-time high-precision detection. Therefore, this paper prop...

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Published in:Journal of physics. Conference series Vol. 2179; no. 1; pp. 12034 - 12039
Main Authors: Huang, Yinrong, Wang, Bing, Yuan, Xiemin
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.01.2022
Subjects:
Algorithms
Artificial neural networks
Embedded systems
Hough transformation
Machine learning
Pedestrians
Physics
Real time
Traffic control
Traffic signs
ISSN:1742-6588, 1742-6596
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Abstract Traffic signs are an important part of autonomous driving and intelligent transportation. It provides instructions for pedestrians and vehicles and is critical to road traffic safety. However, existing detection algorithms cannot achieve real-time high-precision detection. Therefore, this paper proposes an algorithm that combines traditional methods with deep learning to detect circular traffic signs. Based on the HSV color space, the red and blue channel images are separated, and the candidate regions of the original image are extracted using the Hough transform. The shallow convolutional neural network (CNN) classifier rejects not traffic signs and classifies traffic signs. Experiments show that the algorithm is real and effective. On the CPU platform, the average accuracy rate is 96.2%, and the detection speed reaches 0.3 s / frame. Under the condition of ensuring the average accuracy rate, the detection speed is greatly reduced. The algorithm achieves the fastest speed, which makes real-time high-precision detection possible. The algorithm is more suitable for vehicle embedded systems.
AbstractList Traffic signs are an important part of autonomous driving and intelligent transportation. It provides instructions for pedestrians and vehicles and is critical to road traffic safety. However, existing detection algorithms cannot achieve real-time high-precision detection. Therefore, this paper proposes an algorithm that combines traditional methods with deep learning to detect circular traffic signs. Based on the HSV color space, the red and blue channel images are separated, and the candidate regions of the original image are extracted using the Hough transform. The shallow convolutional neural network (CNN) classifier rejects not traffic signs and classifies traffic signs. Experiments show that the algorithm is real and effective. On the CPU platform, the average accuracy rate is 96.2%, and the detection speed reaches 0.3 s / frame. Under the condition of ensuring the average accuracy rate, the detection speed is greatly reduced. The algorithm achieves the fastest speed, which makes real-time high-precision detection possible. The algorithm is more suitable for vehicle embedded systems.
Author Huang, Yinrong
Wang, Bing
Yuan, Xiemin
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10.1109/SIPROCESS.2016.7888348
10.1109/ICIP.2012.6466896
10.1007/s00521-011-0718-z
10.1109/ICSensT.2018.8603600
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SubjectTerms Algorithms
Artificial neural networks
Embedded systems
Hough transformation
Machine learning
Pedestrians
Physics
Real time
Traffic control
Traffic signs
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