孟繁星;于瓅;

• 1:安徽理工大学计算机科学与工程系

摘要(Abstract)：

针对目前的交通标志识别模型检测速度慢、精通过的问题，提出了基于YOLOv5-EA的交通标志识别算法。首先选择YOLOv5作为基础模型，根据交通标志尺寸小的特点，引入了有效通道注意力机制(Efficient Channel Attention)，不仅避免降维和跨通道交互保持性能，还显著降低了模型的复杂度，提高了特征提取的能力；其次通过增加小尺度检测层，提高模型小目标检测的能力;最后在骨干网络中使用BSConv代替了正则卷积，减少了模型的参数。实验结果表明，在公开的TT100K数据集的基础上进行调整后，对改进前后的模型进行训练对比，改进后YOLOv5-EA模型的mAP为87%，较原始的YOLOv5模型提升了3.7%，训练中的损失降低了34%，能够更快速、准确的检测到交通标志。

关键词(KeyWords)： YOLOv5;ECA;交通标志;BSConv;TT100K

Abstract:

Keywords:

基金项目(Foundation): 2021安徽省重点研究与开发计划项目(202104d07020010)

作者(Authors): 孟繁星;于瓅;

DOI: 10.15916/j.issn1674-3261.2022.05.005

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