Research on traffic sign detection algorithm based on improved SSD in complex environments

作者：Hong Zhang, Wei Zhang, Wanqi Wang, Xinlong Li, Anyu Zhang · 发表于：Measurement Science and Technology · 年份：2024 · DOI：10.1088/1361-6501/ad6b41 · 被引用次数：8 · 研究领域：Advanced Neural Network Applications、Infrastructure Maintenance and Monitoring、Hand Gesture Recognition Systems

Abstract In complex traffic sign environments, detection challenges include uneven size distribution, insufficient valid information, and difficulties in identifying targets under resource constraints, leading to missed and false detections. This study proposes an enhanced lightweight traffic sign detection algorithm based on single shot multibox detector (SSD). By replacing the original backbone network with MobileNetV2, the model is streamlined to have fewer parameters, which improves generalization in complex environments. This modification significantly boosts the recall rate, achieving a better balance between precision and recall. By introducing the feature pyramid network combined with the convolutional block attention module attention mechanism, the detailed and semantic information between deep and shallow layers is fully integrated, reducing the loss of feature information, thus enhancing the strengthening of key information of traffic signs and the adaptability to different scales of traffic signs. Finally, by integrating the cross-attention mechanism, the algorithm’s anti-interference ability in complex environments is improved, and the positioning accuracy of traffic signs is enhanced by capturing the dependency between different positions. Through ablation experiments and comparative experiments on a public traffic sign dataset, our improved SSD algorithm achieved an mean average precision (mAP) of 89.97%. Compared with the original algorithm, the mAP increased ...