This paper proposes a lane detection method using a light-weight convolutional neural network model as a feature extractor exploiting the potential of deep ...
Oct 9, 2023 · The first and most popular way to detect lane lines is by using image segmentation. It's actually something I worked on for many months as a self-driving car ...
Vision-based lane detection systems often consist of three main components: preprocessing, feature extraction, and post-processing including curve fitting [1].
A deep learning approach for lane marking detection applying encode ...
pubmed.ncbi.nlm.nih.gov › ...
Mar 3, 2023 · DenselyBased Spatial Clustering of Application with Noise (DBSCAN) is employed to connect the predicted lane pixels and to get the final output.
This study presents the Encode-Decode Instant Segmentation Network (EDIS-Net) as a DL methodology for detecting lane marking under various environmental ...
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In this paper, a vision based robust lane detection system using a novel 1-dimensional deep learning approach is proposed.
Duration: 13:41
Posted: Jul 2, 2021
Posted: Jul 2, 2021
A Deep Learning Approach for Lane Detection - ResearchGate
www.researchgate.net › publication › 35...
The lane centering function uses image processing techniques such as Canny edge detection or Artificial Intelligence (AI) to determine the position of the lane ...
One of the main objectives of this paper is to replace handcrafted lane marking feature extraction method by an equivalent deep convolutional neural network to ...
The research work explores the impact of incorporating temporal information from a road scene on a lane detection model's predictive performance. A post- ...