19 June 2017 Lane marking detection based on waveform analysis and CNN
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Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 1044316 (2017); doi: 10.1117/12.2280245
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore
Abstract
Lane markings detection is a very important part of the ADAS to avoid traffic accidents. In order to obtain accurate lane markings, in this work, a novel and efficient algorithm is proposed, which analyses the waveform generated from the road image after inverse perspective mapping (IPM). The algorithm includes two main stages: the first stage uses an image preprocessing including a CNN to reduce the background and enhance the lane markings. The second stage obtains the waveform of the road image and analyzes the waveform to get lanes. The contribution of this work is that we introduce local and global features of the waveform to detect the lane markings. The results indicate the proposed method is robust in detecting and fitting the lane markings.
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Yang Yang Ye, Hou Jin Chen, Xiao Li Hao, "Lane marking detection based on waveform analysis and CNN", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044316 (19 June 2017); doi: 10.1117/12.2280245; http://dx.doi.org/10.1117/12.2280245
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Roads

Polonium

Image analysis

Gaussian filters

Hough transforms

Image enhancement

Image filtering

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