文献类型：期刊文献

英文题名：A traffic signal recognition algorithm based on self-paced learning and deep learning

作者：Wang, Tingmei[1]; Shen, Haiwei[1]; Xue, Yuanjie[1]; Hu, Zhengkun[1]

第一作者：王廷梅

通讯作者：Wang, Tingmei

机构：[1] College of Applied Science and Technology, Beijing Union University, Beijing, 102200, China

第一机构：北京联合大学应用科技学院

年份：2020

卷号：25

期号：2

起止页码：239-244

外文期刊名：Ingenierie des Systemes d'Information

收录：EI(收录号：20202708899531);Scopus(收录号：2-s2.0-85087276053)

基金：The work was supported by The Research and Practice on The Through-Type Training Mode of High-End Technologies and Personnel with Technical Skills of Beijing. Beijing Municipal Education Commission, Beijing. China (Grant No.: 2018-54), The Scientific Research Program Project of Beijing Education Commission on Traffic Light Recognition Based on Deep Learning for Intelligent Vehicles (Grant No.: KM201911417003), The Famous Teachers of Beijing and The Academic Research Project of Vocational Education and Industry Research Center, College of Applied and Science, Beijing Union University.

语种：英文

外文关键词：Classification (of information) - Complex networks - Convolutional neural networks - Image recognition - Learning algorithms - Signal processing - Support vector machines - Traffic signals - Traffic signs

摘要：Traffic signal recognition is a critical function of the intelligent vehicle system (IVS). Many algorithms can achieve a high accuracy in traffic signal recognition. But these algorithms have poor generalization ability, and their recognition rates vary greatly with datasets. These defects hinder their application in unmanned driving. To solve the problem, this paper introduces self-paced learning (SPL) to the image recognition of traffic signs. Based on complexity, the SPL automatically classifies samples into multiple sets. If machine learning (ML) algorithm is trained by the sample sets in ascending order of complexity, a universal computing model will be obtained, and the ML algorithm will have a better generalization ability. Here, the support vector machine (SVM) is adopted as the classifier for traffic sign detection, and the convolutional neural network (CNN) is employed as the classifier for traffic sign recognition. Then, the two classifiers were trained by the SPL on two public datasets: German Traffic Sign Detection Benchmark (GTSDB) and German Traffic Sign Recognition Benchmark (GTSRB). The model obtained through the training was tested on Belgium Traffic Sign Detection Benchmark (BTSDB) and KITTI datasets. The results show that the obtained computing model achieved similar accuracy on the training sets and test sets. Hence, the SPL can indeed enhance the generalization ability of ML algorithms, and promote the application of CNN. SVM, and other ML algorithms in unmanned driving. ? 2020 International Information and Engineering Technology Association. All rights reserved.