基于改进YOLOv3的实时交通标志检测算法

针对目前我国智能驾驶辅助系统识别道路交通标志检测速度慢、识别精度低等问题，提出一种基于YOLOv3的改进的道路交通标志检测算法。首先，将MobileNetv2作为基础特征提取网络引入YOLOv3以形成目标检测网络模块MN-YOLOv3，在MN-YOLOv3主干网络中引入两条Down-up连接进行特征融合，从而减少检测算法的模型参数，提高了检测模块的运行速度，增强了多尺度特征图之间的信息融合；然后，根据交通标志目标形状的特点，使用K-Means++算法产生先验框的初始聚类中心，并在边界框回归中引入距离交并比（DIOU）损失函数来将DIOU与非极大值抑制（NMS）结合；最后，将感兴趣区域（ROI）与上下文信息通过ROI Align统一尺寸后融合，从而增强目标特征表达。实验结果表明，所提算法性能更好，在长沙理工大学中国交通标志检测（CCTSDB）数据集上的平均准确率均值（mAP）可达96.20%。相较于Faster R-CNN、YOLOv3、Cascaded R-CNN检测算法，所提算法拥有具有更好的实时性和更高的检测精度，对各种环境变化具有更好的鲁棒性。

Real-time traffic sign detection algorithm based on improved YOLOv3

Aiming at the problems of slow detection and low recognition accuracy of road traffic signs in Chinese intelligent driving assistance system， an improved road traffic sign detection algorithm based on YOLOv3 （You Only Look Once version 3） was proposed. Firstly， MobileNetv2 was introduced into YOLOv3 as the basic feature extraction network to construct an object detection network module MN-YOLOv3 （MobileNetv2-YOLOv3）. And two Down-up links were added to the backbone network of MN-YOLOv3 for feature fusion， thereby reducing the model parameters， and improving the running speed of the detection module as well as information fusion performance of the multi-scale feature maps. Then， according to the shape characteristics of traffic sign objects， K-Means++ algorithm was used to generate the initial cluster center of the anchor， and the DIOU （Distance Intersection Over Union） loss function was introduced to combine DIOU and Non-Maximum Suppression （NMS） for the bounding box regression. Finally， the Region Of Interest （ROI） and the context information were unified by ROI Align and merged to enhance the object feature expression. Experimental results show that the proposed algorithm has better performance， and the mean Average Precision （mAP） of the algorithm on the dataset CSUST （ChangSha University of Science and Technology） Chinese Traffic Sign Detection Benchmark （CCTSDB） can reach 96.20%. Compared with Faster R-CNN （Region Convolutional Neural Network）， YOLOv3 and Cascaded R-CNN detection algorithms， the proposed algorithm has better real-time performance， higher detection accuracy， and is more robustness to various environmental changes.