基于Darknet23和特征融合的交通标志检测方法

Traffic sign’s detection method based on Darknet23 and feature fusion

道路交通标志检测是智能交通的重要环节之一,针对交通标志检测存在背景复杂、目标较小、检测速度慢等问题,选取工业界青睐的YOLOv3模型提出一种改进的检测方法。利用双向特征金字塔结构实现图像低、中、高层特征语意信息的双向融合,提升低层预测目标的分类和高层预测目标的定位能力;将原模型的主干特征提取网络进行改进,提出Darknet23网络,以提高网络的提取能力和减少计算量;根据目标形状的特点,使用K-means聚类算法得到用于训练合适的锚点框,并在边框回归中引入灵活性更强的Lα-CIOU损失函数,使网络朝着预测框与真实框重叠度较高的方向去优化。实验结果表明,该方法在CCTSDB数据集上mAP@0.75达到86.10%、mAP@0.5:0.05:0.95达到70.017%,相比原网络分别提升10.17%和5.656%,参数量减少3 622 091,速度提升8.27 f/s,且优于SSD和Faster RCNN等主流的检测网络。

Road traffic sign′s detection is one of the important links of intelligent transportation.A detection method based on the improved YOLOv3 model by the industry is proposed for the problems of complex background,small targets and slow detection speed in traffic sign detection.The method used a bidirectional feature pyramid structure to achieve bidirectional fusion of semantic information of low,middle and high level features of images to improve the classification of low-level prediction targets and the localization of high-level prediction targets.The main feature extraction network of the original model is improved,and the Darknet23 network is proposed to improve the extraction ability of the network and reduce the computational burden.According to the characteristics of the target shape,the K-means clustering algorithm for training the appropriate anchor frames and a more flexible Lα-CIOU loss function is introduced into the bounding box regression to make the network optimize towards a higher degree of overlap between the prediction boxes and the ground-truth boxes.The experimental results show that the method reaches 86.10% mAP@0.75 and 70.017% mAP@0.5:0.05:0.95 on the CCTSDB dataset,which are 10.17% and 5.656% higher than the original network,the number of parameters is reduced by 3 622 091 and the speed is improved 8.27 f/s,which is better than mainstream detection networks such as SSD and Faster RCNN.