A Hierarchical Approach for Traffic Sign Recognition Based on Shape Detection and Image Classification

Eric Hsueh Chan Lu, Michal Gozdzikiewicz, Kuei Hua Chang, Jing Mei Ciou

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)


In recent years, the development of self-driving cars and their inclusion in our daily life has rapidly transformed from an idea into a reality. One of the main issues that autonomous vehicles must face is the problem of traffic sign detection and recognition. Most works focusing on this problem utilize a two-phase approach. However, a fast-moving car has to quickly detect the sign as seen by humans and recognize the image it contains. In this paper, we chose to utilize two different solutions to solve tasks of detection and classification separately and compare the results of our method with a novel state-of-the-art detector, YOLOv5. Our approach utilizes the Mask R-CNN deep learning model in the first phase, which aims to detect traffic signs based on their shapes. The second phase uses the Xception model for the task of traffic sign classification. The dataset used in this work is a manually collected dataset of 11,074 Taiwanese traffic signs collected using mobile phone cameras and a GoPro camera mounted inside a car. It consists of 23 classes divided into 3 subclasses based on their shape. The conducted experiments utilized both versions of the dataset, class-based and shape-based. The experimental result shows that the precision, recall and mAP can be significantly improved for our proposed approach.

出版狀態Published - 2022 7月 1

All Science Journal Classification (ASJC) codes

  • 分析化學
  • 資訊系統
  • 生物化學
  • 原子與分子物理與光學
  • 儀器
  • 電氣與電子工程


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