Research on classification and detection of traffic signs based on tags combined with real scenes

• Published in: Diànzǐ jìshù yīngyòng Vol. 48; no. 3; pp. 27 - 31
• Main Authors: Zhang Cheng, Zhang Ruibin, Wang Shudao
• Format: Journal Article
• Language: Chinese
• Published: National Computer System Engineering Research Institute of China 01.03.2022
• Subjects: automatic driving; data enhancement; dcnn; detection; traffic signs
• DOI: 10.16157/j.issn.0258-7998.211840
• ISSN: 0258-7998

There are a lot of researches on traffic signs in the safe driving and automatic driving of vehicles. Due to the wide variety of traffic signs and the influence of various factors, the classification and detection of traffic signs is also a challenging problem. To this end, a traffic sign classification and detection method combining tags with real road scenes is proposed. The method is divided into a data generation part and a target detection part. Experimental results show that the use of this method to generate training data can effectively train deep convolutional neural networks to achieve classification and detection of traffic signs in real scenes, and the optimized detection model has a smaller size and faster speed than the model mentioned in the article.