Performance enhancement techniques for traffic sign recognition using a deep neural network

• Published in: Multimedia tools and applications Vol. 79; no. 29-30; pp. 20545 - 20560
• Main Authors: Khan, Jameel Ahmed, Chen, Yunfan, Rehman, Yawar, Shin, Hyunchul
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Image enhancement; Multimedia Information Systems; Neural networks; Object recognition; Optimization; Optimization techniques; Performance enhancement; Signs; Special Purpose and Application-Based Systems; Street signs; Traffic control; Traffic signs; Deep neural network; YOLOv3; Traffic sign recognition; Pre-processing; Optimization

An advanced traffic sign recognition (ATSR) system using novel pre-processing techniques and optimization techniques has been proposed. During the pre-processing of input road images, color contrasts are enhanced and edges are made clearer, for easier detection of small-sized traffic signs. YOLOv3 h...