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http://dx.doi.org/10.21289/KSIC.2020.23.6.979

A Study on the Detection Method of Lane Based on Deep Learning for Autonomous Driving  

Park, Seung-Jun (Graduate School Of Automotive Engineering Kookmin University)
Han, Sang-Yong (Graduate School Of Automotive Engineering Kookmin University)
Park, Sang-Bae (Korea Polytechnics)
Kim, Jung-Ha (Graduate School Of Automotive Engineering Kookmin University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.23, no.6_2, 2020 , pp. 979-987 More about this Journal
Abstract
This study used the Deep Learning models used in previous studies, we selected the basic model. The selected model was selected as ZFNet among ZFNet, Googlenet and ResNet, and the object was detected using a ZFNet based FRCNN. In order to reduce the detection error rate of FRCNN, location of four types of objects detected inside the image was designed by SVM classifier and location-based filtering was applied. As simulation results, it showed similar performance to the lane marking classification method with conventional 경계 detection, with an average accuracy of about 88.8%. In addition, studies using the Linear-parabolic Model showed a processing speed of 165.65ms with a minimum resolution of 600 × 800, but in this study, the resolution was treated at about 33ms with an input resolution image of 1280 × 960, so it was possible to classify lane marking at a faster rate than the previous study by CNN-based End to End method.
Keywords
Deep learning; Faster R-CNN; Machine learning; Support vector machine; Unmanned vehicle;
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