改进YOLOv7的交通标志检测算法

Improved YOLOv7 Algorithm for Traffic Sign Detection

自动驾驶技术的快速发展,导致对交通标志检测技术的要求日益提高.为解决YOLOv7算法在识别小目标时误检、漏检等问题,本文提出一种基于注意力机制的交通标志检测模型YOLOv7-PC.首先通过K-means++聚类算法对交通标志数据集进行聚类,获得适用于检测交通标志的锚框;其次在YOLOv7主干特征提取网络中引入坐标注意力机制,将交通标志的横向和纵向信息嵌入到通道中,使生成的特征信息具有交通标志的坐标信息,加强有效特征的提取;最后在加强特征提取网络中引入空洞空间金字塔池化,捕获交通标志多尺度上下文信息,在保证交通标志小目标分辨率的同时,进一步扩大卷积的感受野.在中国交通标志检测数据集(CCTSDB)上的实验表明,本文算法增强了识别小目标的能力,相较于YOLOv7模型,本文算法的mAP、召回率平均分别提高了5.22%、9.01%,是一种有效的交通标志检测算法.

The rapid development of autonomous driving technology has led to increasing requirements for traffic sign detection technologies.In order to solve the problems of false detection and missed detection of the YOLOv7 algorithm in identifying small targets,this study proposes a traffic sign detection model based on an attention mechanism,namely YOLOv7-PC.Firstly,a K-means++clustering algorithm is used to cluster the traffic sign dataset to obtain anchor boxes suitable for detecting traffic signs.Secondly,the coordinate attention mechanism is introduced into the YOLOv7 backbone feature extraction network,and the horizontal and vertical information of traffic signs are embedded into the channel so that the generated feature information has the coordinate information of traffic signs,and the extraction of effective features is enhanced.Finally,the atrous spatial pyramid pooling is introduced in the enhanced feature extraction network to capture multi-scale context information of traffic signs,which ensures the resolution of small targets of traffic signs and expand the receptive field of the convolutional nucleus.Experiments on the China traffic sign detection dataset(CCTSDB)show that the proposed algorithm enhances the ability to recognize small targets.Compared with the YOLOv7 model,the proposed algorithm has an average improvement of 5.22%in mAP and 9.01%in Recall,making it an effective traffic sign detection algorithm.