In this approach, an encoder-decoder deep learning architecture generates binary segmentation of lanes, then the binary segmentation map is further processed ...
In this approach, an encoder-decoder deep learning architecture generates binary segmentation of lanes, then the binary segmentation map is further processed to ...
This work proposes integrating traditional computer vision techniques and deep learning methods to develop a reliable benchmarking framework for lane detection ...
Nov 17, 2020 · SegNet is trained to output a lane binary segmentation map, which indicates which pixels in the input image belong to a lane, any lane, and ...
The edge detection is a primary technique in image processing which gives the form of the object in an image. The absolute result of edge detection method can ...
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These annotated images then enabled the training of a U-Net inspired network and achieved real-time and accurate road marking detection. 2) FCN with ...
This work addresses the current lack of data for determining lane instances, which are needed for various driving manoeuvres. The main issue is the time-.
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 ...
Jan 3, 2024 · In this study, we provide an effective lane detection method based on semantic segmentation to identify lane lines in a high-dimensional dataset ...
This paper describes and analyzes the lane line departure warning systems, image processing algorithms and semantic segmentation methods for lane line ...