为解决YOLOv3算法在检测道路交通灯时存在的漏检率高、召回率低等问题,提出一种基于优化YOLOv3算法的交通灯检测方法。首先,采用K-means算法对数据进行聚类分析,结合聚类结果和交通灯标签的统计结果,确定先验框的宽高比及其数量。然后,根据交通灯尺寸特点,精简网络结构,分别将8倍降采样信息、16倍降采样信息与高层语义信息进行融合,在两个尺度上建立目标特征检测层。同时,为了避免交通灯特征随着网络的加深而消失的问题,分别减少两个目标检测层前的两组卷积层,简化特征提取步骤。最后,在损失函数中,利用高斯分布特性评估边界框的准确性,以提升对交通灯检测的精度。实验结果显示,优化YOLOv3算法的检测速度可达30 frame/s,平均精准度较原网络提升9个百分点,可以有效完成对交通灯的检测。

To solve the problems of high missed-detection rate and low recall rate existed in the YOLOv3 algorithm for detecting traffic lights, a traffic light detection method based on the optimized YOLOv3 algorithm is proposed. First, the K-means algorithm is used to cluster the data. By combining the clustering results with the statistical results of traffic light labels, the number and the width-height ratios of the prior boxes are determined. Then, the network structure is simplified according to the size characteristics of traffic lights. The 8× downsampling information and the 16× downsampling information are fused with high-level semantic information, and the object feature detection layer is established on two scales. Meanwhile, to avoid the disappearance problem of traffic light features with the deepening of the network, two sets of convolution layers are reduced before two object-detection layers, and thus the feature extraction steps are simplified. Finally, in the loss function, Gaussian distribution characteristics are used to evaluate the accuracy of the boundary box to improve the precision of traffic light detection. The experimental results reveal that the detection speed of the optimized YOLOv3 algorithm can reach 30 frames/s and the average precision is 9 percent higher than that of the original network, which effectively completes the detection of traffic lights.