A Traffic Signal Recognition Algorithm Based on Self-paced Learning and Deep Learning

Abstract

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.

Low-Light Image Enhancement and Target Detection Based on Deep Learning

“…At present, many computer vision systems are installed in outdoor environments and lowtemperature water environments. Their performance is easily affected by light distribution [18][19][20][21][22][23][24]. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects.…”

Automatic Recognition of Traffic Signs Based on Visual Inspection

“…Traffic sign recognition is a research hotspot in the application of visual navigation and computer vision in intelligent driving [1,2]. Under multiple constraints, the recognition of traffic signs needs to realize various goals with a high accuracy through complex implementation methods.…”