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<p>With advancements in computer vision and artificial intelligence, traffic sign recognition systems have become essential in advanced driver assistance and autonomous driving systems. These systems enable the precise detection of key road information. However, recognizing small traffic signs in real-world scenarios remains a significant challenge due to their limited size and features. In this study, we propose YOLO-TSR, an efficient approach for detecting small traffic signs, inspired by the YOLOv8 framework. This method offers three major contributions: (1) We introduce an efficient attention mechanism in the Backbone to enhance focus on small targets; (2) We propose a downsampling process using slicing and reassembling operations in the backbone, which preserve information and improve feature extraction for small objects; (3) We refine the upsampling process in the head by applying the content-aware CARAFE operation, which enhances the model’s detection performance. Experiments on the challenging TT100K and CCTSDB2021 datasets show that YOLO-TSR achieves a mAP50 of 72.73% and a mAP50-95 of 56.57% on TT100K, and a mAP50 of 87.86% and a mAP50-95 of 57.78% on CCTSDB2021, surpassing the performance of the original YOLOv8n on both datasets. Additionally, this method is real-time and demonstrates great potential for applications in advanced driver assistance systems and autonomous driving systems.</p>

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Enhancing small traffic sign recognition based on an improved YOLOv8 algorithm

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Xiaoqing Zhang,
  • Weixi Wang,
  • Qi Wang,
  • Liang Shan,
  • Satoshi Yamane,
  • Bochao Zheng,
  • Sifan Liu,
  • Tianxing Shen,
  • Dongming Ge

摘要

With advancements in computer vision and artificial intelligence, traffic sign recognition systems have become essential in advanced driver assistance and autonomous driving systems. These systems enable the precise detection of key road information. However, recognizing small traffic signs in real-world scenarios remains a significant challenge due to their limited size and features. In this study, we propose YOLO-TSR, an efficient approach for detecting small traffic signs, inspired by the YOLOv8 framework. This method offers three major contributions: (1) We introduce an efficient attention mechanism in the Backbone to enhance focus on small targets; (2) We propose a downsampling process using slicing and reassembling operations in the backbone, which preserve information and improve feature extraction for small objects; (3) We refine the upsampling process in the head by applying the content-aware CARAFE operation, which enhances the model’s detection performance. Experiments on the challenging TT100K and CCTSDB2021 datasets show that YOLO-TSR achieves a mAP50 of 72.73% and a mAP50-95 of 56.57% on TT100K, and a mAP50 of 87.86% and a mAP50-95 of 57.78% on CCTSDB2021, surpassing the performance of the original YOLOv8n on both datasets. Additionally, this method is real-time and demonstrates great potential for applications in advanced driver assistance systems and autonomous driving systems.