Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis

• Published in: Applied sciences 2020-10, Vol.10 (19), p.6997
• Main Authors: Tai, Shao-Kuo, Dewi, Christine, Chen, Rung-Ching, Liu, Yan-Ting, Jiang, Xiaoyi, Yu, Hui
• Format: Article
• Language: English
• Subjects: Algorithms; Boxes; Deep learning; Design and construction; Experiments; Guttman scaling; Identification; Identification and classification; Machine learning; Machine vision; Methods; Neural networks; object detection; scale analysis; Signs; SPP; Support systems; Technology application; Traffic; Traffic control; Traffic safety; Traffic signs; Traffic signs and signals; TSD; TSR; Yolo V3
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10196997

In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.