Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/25269
Title: Road Traffic Sign Detection and Recognition using Adaptive Color Segmentation and Deep Learning
Authors: KhabiriKhatiri, Roozbeh
Abd Latiff, Abd Latiff
Mohamad, Ahmad Sabry
UniKL BMI
Keywords: Traffic sign detection
Adaptive color segmentation
Traffic sign recognition
Deep Learning
UniKL BMI
Issue Date: 2021
Abstract: Traffic sign detection and recognition (TSDR) is one of the main area of research in autonomous vehicles and Advanced Driving Assistance System (ADAS). In this paper a method is proposed to detect and classify the prohibitory subset of German Traffic Sign data set. The traffic sign detection module utilizes adaptive color segmentation based on mean saturation value of local neighborhood, and Circular Hough Transform (CHT), to detect the traffic signs of input images. After the detection stage a validation module is placed to reject the false alarms given by the system. For the recognition stage a deep Convolutional Neural Network (CNN) is trained to classify the detected signs. The proposed detection and recognition modules are evaluated on German Traffic Sign Detection Benchmark (GTSDB) and German Traffic Sign Recognition Benchmark (GTSRB) respectively and achieved 95.73% detection rate and 99.93% recognition rate. Furthermore, the proposed system is immune to change of illumination and is able to detect damaged, occluded and distorted traffic signs under complex traffic environment.
URI: http://hdl.handle.net/123456789/25269
Appears in Collections:Conference Papers

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