Real-time chinese traffic warning signs recognition based on cascade and CNN

Abstract

Warning signs are of great significance to traffic safety. In this paper, a real-time recognition method for Chinese Traffic Warning Signs (CTWS) is proposed. CTWS are all triangles with yellow background, black border and black pattern. Their similarity is conducive to the localization task of object detection but adverse to the classification task of object detection. After analyzing the characteristics of these signs, real-time recognition for CTWS is carried out by employing Cascade classifier and Convolutional Neural Network (CNN). A Cascade classifier with 9 layers is trained with local binary patterns to locate the CTWS in frames. And a 10-layer CNN model is built to determine the specific category of the signs located by the Cascade classifier. We evaluate the method on CCTSDB-based dataset and GTSDB, and experiments show that the proposed method can perform accurate recognition at an average speed of 81.79fps without GPU. Since the proposed method only needs to call CNN that requires vast computing power in a small number of frames containing CTWS while performing real-time recognition, it can effectively save the valuable on-board computing resources compared with other object detection algorithms that is purely based on CNN such as YOLOv3, YOLOv3-tiny and Faster R-CNN.