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计算机工程 ›› 2025, Vol. 51 ›› Issue (7): 90-99. doi: 10.19678/j.issn.1000-3428.0069409

• 人工智能与模式识别 • 上一篇    下一篇

基于DMC-YOLO的交通标志实时检测算法

栾孟娜*(), 郑秋梅, 王风华   

  1. 中国石油大学(华东)计算机科学与技术学院,山东 青岛 266580
  • 收稿日期:2024-02-22 出版日期:2025-07-15 发布日期:2024-06-26
  • 通讯作者: 栾孟娜
  • 基金资助:
    国家自然科学基金(52074341); 国家自然科学基金(51874340); 中央高校基本科研业务费专项资金(19CX02030A)

Real-time Traffic Sign Detection Algorithm Based on DMC-YOLO

LUAN Mengna*(), ZHENG Qiumei, WANG Fenghua   

  1. College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, China
  • Received:2024-02-22 Online:2025-07-15 Published:2024-06-26
  • Contact: LUAN Mengna

摘要:

在交通标志检测中,由于外界环境的干扰以及驾驶场景中交通标志目标较小的特点,导致提升交通标志检测性能一直是一项具有挑战性的任务。提出一种新的交通标志检测算法,其能够在保证实时检测的情况下显著提高模型的检测精度。首先设计一种新的多尺度特征提取网络,引入大尺度特征来增加小目标定位信息,同时设计多尺度特征注意增强模块进一步获得目标的上下文信息。其次,为了降低模型的计算量和复杂度,对原始模型的多尺度检测头进行改进,选取2个大尺度检测头对小目标进行检测。最后,对完全交并比(CIoU)损失函数进行改进,增强算法对小目标的感知能力,同时提高网络的训练效率。将改进后的模型在2个开源的公共数据集上进行实验。实验结果表明,该算法在TT100K和CCTSDB 2021交通标志数据集上对交通标志小目标的检测精度均有提高,在2个数据集的测试集上均值平均精度(mAP)分别达到84.8%和83.6%,较基准模型分别提升了3.0和3.6百分点,具有更高的检测性能和特征提取能力,且满足实时检测的需求。

关键词: 交通标志检测, 多尺度特征融合, 注意力机制, 膨胀卷积, 小目标检测

Abstract:

In traffic sign detection, external environmental interference and the small size of traffic sign targets in driving scenarios hinder detection performance. This paper introduces a novel traffic sign detection algorithm that significantly improves model detection precision while ensuring real-time detection capabilities. This paper initially designs a new multi-scale feature extraction network, incorporating large-scale features to augment small target localization information, and simultaneously designs a multi-scale feature attention enhancement module to further enhance the model′s feature extraction capability. Second, to reduce the computational load and complexity of the model, this paper improves the multi-scale detection heads of the original model by selecting two large-scale detection heads for detecting small targets. Finally, the algorithm modifies the Complete Intersection over Union (CIoU) loss function to enhance its perception of small targets and improve the network′s training efficiency. On two open-source public datasets, namely the TT100K and CCTSDB 2021 traffic sign datasets, the improved model achieves enhanced detection precision for small traffic sign targets, with a mean Average Precision (mAP) of 84.8% and 83.6% on the test sets, respectively. These results show improvements of 3.0 and 3.6 percentage points over the baseline models, respectively, demonstrating the model′s higher detection performance and feature extraction capabilities while meeting the requirements for real-time detection.

Key words: traffic sign detection, multi-scale feature fusion, attention mechanism, dilated convolution, small object detection