arXiv：cs.AI· Reem Abdel-Salam, Rana Mostafa, Ahmed H. Abdel-Gawad·· 17 小时前
RIECNN：面向交通标志识别的实时图像增强 CNN
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition
AI 导读
研究者提出实时图像增强 CNN（RIECNN），用于交通标志识别，在 GTSRB、BTSC 和 rMASTIF 三个基准上均取得最高识别率，分别为 99.75%、99.25% 和 99.55%。该方法的预处理与推理时间合计不超过每张图 1.3 ms，并对亮度与对比度变化等环境干扰表现出鲁棒性。
正文
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 ({\our}) for Traffic Sign Recognition. {\our} 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.
| Comments: | Published in Neural Computing and Applications, Volume 34, pages 6085-6096 (2022) |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.15127 [cs.CV] |
| (or arXiv:2610.15127v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15127 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Neural Computing and Applications 34, 6085-6096 (2022) |
| Related DOI: | https://doi.org/10.1007/s00521-021-06762-5
DOI(s) linking to related resources |
Submission history
From: Reem Abdel-Salam [view email]
[v1]
Sat, 10 Oct 2026 19:01:49 UTC (3,476 KB)
来源：arXiv：cs.AI · arxiv.org