A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

• Published in: Algorithms Vol. 10; no. 4; p. 127
• Main Authors: Zhang, Jianming, Huang, Manting, Jin, Xiaokang, Li, Xudong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2017
• Subjects: Artificial neural networks; Chinese traffic sign; CNNs; Computer vision; CTSD; Feature maps; GTSDB; Image detection; object detection; Real time; Signs; Street signs; Traffic accidents & safety; Traffic control; Traffic signs; YOLOv2
• DOI: 10.3390/a10040127
• ISSN: 1999-4893

Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.