Employing A Fully Convolutional Neural Network For Road Marking Detection
2017 LATIN AMERICAN ROBOTICS SYMPOSIUM (LARS) AND 2017 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR)(2017)
摘要
Since road markings are one of the main landmarks used for traffic guidance, perceiving them may be a crucial task for autonomous vehicles. In visual approaches, road marking detection consists in detecting pixels of an image that corresponds to a road marking. Recently, most approaches have aimed on detecting lane markings only, and few of them proposed methods to detect other types of road markings. Moreover, most of those approaches are based on local gradient, which provides noisy detections caused by cluttered images. In this paper, we propose an alternative approach based on a deep Fully Convolutional Neural Network (FCNN) with an encoder-decoder architecture for road marking detection and segmentation. The experimental results reveal that the proposed approach can detect any road marking type in a high level of accuracy, resulting in a smooth segmentation.
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关键词
visual approaches,road marking detection,deep fully convolutional neural network,autonomous vehicles,pixels detection,FCNN,encoder-decoder architecture,road marking segmentation
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