Two‐stage traffic sign detection and recognition based on SVM and convolutional neural networks. Issue 5 (10th March 2020)

• Record Type: Journal Article
• Authors: Hechri, Ahmed; Mtibaa, Abdellatif
• Is Part Of: IET image processing. Volume 14:Issue 5(2020)
• Page Start: 939
• Page End: 946
• Publication Date: 2020-03-10
• Subjects: feature extraction -- driver information systems -- road traffic -- object detection -- road safety -- object recognition -- support vector machines -- convolutional neural nets -- image classification; German traffic sign recognition benchmark datasets -- recognition rate -- stage traffic sign detection -- convolutional neural network -- advanced driver assistance systems -- safety -- road traffic scenes -- real‐time traffic sign detection -- traffic situation -- linear support vector machines -- German traffic sign detection benchmark datasets; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0634
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Nowadays, traffic sign recognition is the most important task of advanced driver assistance systems since it improves the safety and comfort of drivers. However, it remains a challenging task due to the complexity of road traffic scenes. In this study, a novel two‐stage approach for real‐time traffic sign detection and recognition in a real traffic situation was proposed. The first stage aims to detect and classify the detected traffic signs into circular and triangular shape using HOG features and linear support vector machines (SVMs). The main objective of the second stage is to recognise the traffic signs using a convolutional neural network into their subclasses. The performance of the whole process is tested on German traffic sign detection benchmark (GTSDB) and German traffic sign recognition benchmark (GTSRB) datasets. Experimental results show that the obtained detection and recognition rate is comparable with those reported in the literature with much less complexity. Furthermore, the average processing time demonstrates its suitability for real‐time processing applications.