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A Study on Candidate Lane Detection using Hybrid Detection Technique  

Park, Sang-Joo (Department of Internet Information, Ansan University)
Oh, Joong-Duk (School of Computer, Information, and Communication Engineering, Sangji University)
Park, Roy C. (Division of Computer Engineering, Dongseo University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.17, no.1, 2016 , pp. 18-25 More about this Journal
Abstract
As more people have cars, the threat of traffic accidents is posed on men and women of all ages. The main culprit of traffic accidents is driving while intoxicated or drowsy. The method to recognize and prevent the cause of traffic accidents is to use lane detection. In this study, a total of 4,000 frames (day image: 2,900 frames, night image: 1,100 frames) were used to test lane detection. According to the test, in the case of day image, when the threshold of Sobel edge detection technique was detected with second-order differential equation, there was the highest candidate lane detection rate which was 86.1%. In the threshold of Canny edge detection technique, the highest detection rate of 88.0% was found at Low=50, and High=300. In the case of night image, the threshold of Sobel edge detection technique, when horizontal calculation and vertical calculation had second-order differential equation, and when horizontal-vertical calculation had 1.5th-order differential equation, there was the highest detection rate which was 83.1%. In the threshold of Canny edge detection technique, the highest detection rate of 89.9% was found at Low=50, and High=300.
Keywords
Lane detection; Edge Detection; Advance Driver Assistant Systems; Lane Departure Warning System;
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