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RIECNN: real-time image enhanced CNN for traffic sign recognition

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Abstract

Traffic sign recognition plays a crucial role in the development of autonomous cars to reduce the accident rate and promote road safety. It has been a necessity to address traffic signs that are affected significantly by the environment as well as poor real-time performance for deep-learning state-of-the-art algorithms. In this paper, we introduce Real-Time Image Enhanced CNN (RIECNN) for Traffic Sign Recognition. RIECNN is a real-time, novel approach that tackles multiple, diverse traffic sign datasets, and out-performs the state-of-the-art architectures in terms of recognition rate and execution time. Experiments are conducted using the German Traffic Sign Benchmark (GTSRB), the Belgium Traffic Sign Classification (BTSC), and the Croatian Traffic Sign (rMASTIF) benchmark. Experimental results show that our approach has achieved the highest recognition rate for all Benchmarks, achieving a recognition accuracy of 99.75% for GTSRB, 99.25% for BTSC and 99.55% for rMASTIF. In terms of latency and meeting the real-time constraint, the pre-processing time and inference time together do not exceed 1.3 ms per image. Not only have our proposed approach achieved remarkably high accuracy with real-time performance, but it also demonstrated robustness against traffic sign recognition challenges such as brightness and contrast variations in the environment.

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Notes

  1. The source code for the developed models and experiments can be found through: https://github.com/RanaMostafaAbdElMohsen/Traffic_Sign_Recognition.

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Author notes

  1. Reem Abdel-Salam and Rana Mostafa contributed equally to this work.

Authors and Affiliations

  1. Computer Engineering Department, Cairo University, Cairo, Egypt

    Reem Abdel-Salam, Rana Mostafa & Ahmed H. Abdel-Gawad

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  1. Reem Abdel-Salam
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  2. Rana Mostafa
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  3. Ahmed H. Abdel-Gawad
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Correspondence to Ahmed H. Abdel-Gawad.

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Abdel-Salam, R., Mostafa, R. & Abdel-Gawad, A.H. RIECNN: real-time image enhanced CNN for traffic sign recognition. Neural Comput & Applic 34, 6085–6096 (2022). https://doi.org/10.1007/s00521-021-06762-5

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  • DOI: https://doi.org/10.1007/s00521-021-06762-5

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