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

Eric Hsueh-Chan Lu1, Michal Gozdzikiewicz1, Kuei-Hua Chang1

  • 1Department of Geomatics, National Cheng Kung University, Tainan City 701, Taiwan.

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Summary
This summary is machine-generated.

This study enhances autonomous vehicle safety by improving traffic sign recognition. A novel two-phase deep learning approach using Mask R-CNN and Xception significantly boosts detection accuracy and classification performance.

Keywords:
computer visiondeep learningimage recognitionobject detectiontraffic sign

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Autonomous vehicles require robust traffic sign detection and recognition for safe operation.
  • Current methods often struggle with real-time performance for fast-moving vehicles.
  • A two-phase approach is common, but performance can be further optimized.

Purpose of the Study:

  • To develop and evaluate a novel two-phase deep learning system for traffic sign detection and classification.
  • To compare the proposed method against the state-of-the-art YOLOv5 detector.
  • To improve precision, recall, and mean Average Precision (mAP) in traffic sign recognition.

Main Methods:

  • Utilized Mask R-CNN for shape-based traffic sign detection in the first phase.
  • Employed the Xception model for traffic sign classification in the second phase.
  • Collected and utilized a dataset of 11,074 Taiwanese traffic signs captured via mobile and GoPro cameras.

Main Results:

  • The proposed Mask R-CNN and Xception approach demonstrated significant improvements in precision, recall, and mAP.
  • Experiments were conducted using both class-based and shape-based dataset versions.
  • The method showed superior performance compared to the YOLOv5 detector in conducted tests.

Conclusions:

  • The proposed two-phase deep learning strategy effectively addresses traffic sign detection and recognition challenges.
  • This approach offers a promising solution for enhancing the safety and reliability of autonomous driving systems.
  • Further research can explore real-world deployment and integration into existing autonomous driving platforms.