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http://dx.doi.org/10.9717/kmms.2021.24.7.860

Implementation of Lane Departure Warning System using Lightweight Deep Learning based on VGG-13  

Kang, Hyunwoo (Dept. of AI Engineering, Korea Polytechnics)
Publication Information
Journal of Korea Multimedia Society / v.24, no.7, 2021 , pp. 860-867 More about this Journal
Abstract
Lane detection is important technology for implementing ADAS or autonomous driving. Although edge detection has been typically used for the lane detection however, false detections occur frequently. To improve this problem, a deep learning based lane detection algorithm is proposed in this paper. This algorithm is mounted on an ARM-based embedded system to implement a LDW(lane departure warning). Since the embedded environment lacks computing power, the VGG-11, a lightweight model based on VGG-13, has been proposed. In order to evaluate the performance of the LDW, the test was conducted according to the test scenario of NHTSA.
Keywords
Lane Detection; Adas; Image Segmentation; Lane Departure Warning;
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