改进YOLOv5s的交通标志识别算法

为了准确且实时地检测到交通标志指示牌,减少交通事故的发生和推动智慧交通的发展,针对现有的道路交通标志检测模型存在的精度不足、权重文件大、检测速度慢的问题,设计了一种基于计算机视觉技术的改进YOLOv5s检测算法YOLOv5s-GC.首先,使用copy-paste进行数据增强后再送入网络进行训练,加强对小目标的检测能力;然后,引入Ghost来构建网络,削减原网络的参数和计算量,实现轻量化模型;最后,将坐标注意力机制(coordinate attention)融合到骨干网络里,增强对待测目标的表示和定位能力,提高识别精度.实验结果表明,YOLOv5s-GC模型相比于原YOLOv5s模型,参数数目减少了12%,检测速度提高了22%,平均精度达到了94.2%,易于部署且能满足实际自动驾驶场景中对识别交通标志的速度和准确度要求.

Improved YOLOv5s Algorithm for Traffic Sign Recognition

This study aims to detect traffic signs accurately and in real time, reduce traffic accidents, and promote intelligent transportation. An improved YOLOv5s detection algorithm, YOLOv5s-GC, based on computer vision technology is designed to solve the problems of insufficient accuracy, large weight files, and slow detection speed of existing detection models for road traffic signs. Firstly, data is enhanced by copy-paste and then sent to the network for training to improve the detection ability of small targets. Then, Ghost is introduced to build the network, reducing the parameters and calculation amount of the original network, and realizing a lightweight network. Finally, the coordinate attention mechanism is added to the backbone network to enhance the representation and positioning of the attention target and improve detection accuracy. The experimental results show that in comparison with the YOLOv5s, the number of parameters of the YOLOv5s-GC network model is reduced by 12%; the detection speed is increased by 22%; the average accuracy reaches 94.2%. The YOLOv5s-GC model is easy to deploy and can meet the speed and accuracy requirements of traffic sign recognition in actual autonomous driving scenarios.