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首页> 外文期刊>Journal of Real-Time Image Processing >Real-time Chinese traffic warning signs recognition based on cascade and CNN
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Real-time Chinese traffic warning signs recognition based on cascade and CNN

机译：基于级联和CNN的实时中国交通警告标志识别

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摘要

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.
机译：警告标志对交通安全具有重要意义。本文提出了一种用于中国交通警报标志（CTWS）的实时识别方法。 CTW是带有黄色背景，黑色边框和黑色图案的三角形。它们的相似性有利于对象检测的本地化任务，但对物体检测的分类任务不利。在分析这些标志的特征之后，通过采用级联分类器和卷积神经网络（CNN）来执行CTWS的实时识别。具有9层的级联分类器具有局部二进制模式的培训，以在帧中定位CTWS。建立了一个10层CNN模型，以确定级联分类器的标志的特定类别。我们评估基于CCTSDB的数据集和GTSDB的方法，实验表明，该方法可以以81.79fps的平均速度在没有GPU的情况下进行准确识别。由于所提出的方法仅需要呼叫CNN，该CNN在执行实时识别的同时在包含CTWS的少量帧中呼叫CNN，因此可以有效地与纯粹基于的其他对象检测算法相比保存有价值的车载计算资源在CNN上，如YOLOV3，YOLOV3-TINY和更快的R-CNN。

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