Lane detection is the task of detecting lanes on a road from a camera.
( Image credit: End-to-end Lane Detection )
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In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented.
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations.
In this paper, a novel and pragmatic approach for lane detection is proposed using a convolutional neural network (CNN) model based on SegNet encoder-decoder architecture.
Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can distinguish different types of lanes.
In this paper, we use lane detection to study modeling and training techniques that yield better performance on real world test drives.
Among the several deep learning architectures, convolutional neural networks (CNNs) outperformed other machine learning models, especially for region proposal and object detection tasks.
Lane detection is an important yet challenging task in autonomous driving, which is affected by many factors, e. g., light conditions, occlusions caused by other vehicles, irrelevant markings on the road and the inherent long and thin property of lanes.
#2 best model for Lane Detection on CULane