基于改进YOLOv5的昏暗小目标交通标志识别

为了实时检测并识别路上的交通标志,针对在不良光照情况影响下小型交通标志的识别精确度较低、误检、漏检严重的问题,提出了一种基于改进YOLOv5的交通标志识别模型.首先在YOLOv5模型的浅层特征图层增加一次concat操作,将浅层的特征信息结合中间特征图层作为一个检测头,有利于小目标交通标志的识别效率.其次将坐标注意力机制添加到YOLOv5模型中,从而提高特征提取的效率.对中国交通标志数据集TT100K进行数据扩充和暗光增强的操作,最后在经过预处理的TT100K数据集上验证本文改进的模型检测效果.实验结果表明本文改进的模型对小目标及昏暗情况的交通标志识别效率有很大的提升.本文改进的YOLOv5模型与最初的YOLOv5模型均在扩充后的数据集上进行训练后的结果相比,在准确率上提升了1.5%,达到了93.4%;召回率提升了6.8%,达到了92.3%; mAP值提高了5.2%,达到了96.2%.

Traffic Sign Recognition for Dim Small Targets Based on Improved YOLOv5

In order to detect and recognize traffic signs on the road in real time, a traffic sign recognition model based on improved YOLOv5 is proposed to solve the problems of low recognition accuracy and serious false detection and missing detection of small traffic signs under the influence of poor lighting. First, a concat operation is added to the shallow feature layer of the YOLOv5 model, and the shallow feature information is combined with the middle feature layer and then serves as a detection head, which is conducive to the recognition efficiency of small traffic signs. Secondly, a coordinate attention mechanism is added to the YOLOv5 model to improve the efficiency of feature extraction. The Chinese traffic sign dataset TT100K is expanded, and the dark light is enhanced. Finally, the improved model detection effect is verified on the preprocessed TT100K dataset. The experimental results show that the recognition efficiency of the improved model for small and dim traffic signs is greatly improved. Compared with the results of the original YOLOv5 model trained on the expanded dataset, the accuracy of the improved YOLOv5 model in this study is improved by 1.5%, reaching 93.4%. The recall rate is increased by 6.8%, reaching 92.3%. The mAP value is increased by 5.2%, reaching 96.2%.