Real-time traffic sign detection based on YOLOv2
Huan Zhu, Chongyang Zhang · 2018
Traffic sign detection is an important part of driverless vehicle. high accuracy detection algorithms are difficult to run in real-time. In this paper, we propose a detection model to ease the problem effectively. our model combines three key insights with YOLOv2 to improve the mean average precision(mAP): (1) Focal Loss is used to let our model focus on a sparse set of indistinguishable samples, (2) Inception is used to increase the depth and nonlinearity of network and (3) ResNet is used to ease the difficult in training deep convolutional neural network by adding cross-layer connections. On the German Traffic Sign Detection Benchmark (GTSDB), our model can achieve high accuracy and real-time performance of traffic sign detection at the same time. The recall is 94.46%, the precision is 96.60%, the AUC is 99.75%, the mAP is 88.23% and the average time for processing an image is 0.017s. Results indicate that the modified detection model is competitive compared to others.