Corpus ID: 221136039

Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor

  title={Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor},
  author={Wensheng Cheng and Haowen Luo and Wen Yang and Lei Yu and Wei Li},
Lane marker extraction is a basic yet necessary task for autonomous driving. Although past years have witnessed major advances in lane marker extraction with deep learning models, they all aim at ordinary RGB images generated by frame-based cameras, which limits their performance in extreme cases, like huge illumination change. To tackle this problem, we introduce Dynamic Vision Sensor (DVS), a type of event-based sensor to lane marker extraction task and build a high-resolution DVS dataset for… Expand
1 Citations
Deep Learning in Lane Marking Detection: A Survey
This paper reviews deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Expand


DET: A High-Resolution DVS Dataset for Lane Extraction
This work introduces Dynamic Vision Sensor (DVS), a type of event-based sensor to lane extraction task and builds a high-resolution DVS dataset for lane extraction (DET), which demonstrates that DET is quite challenging for even state-of-the-art lane extraction methods. Expand
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A fast lane detection algorithm, running at 50 fps, is proposed, which can handle a variable number of lanes and cope with lane changes, and is robust against road plane changes, unlike existing approaches that rely on a fixed, predefined transformation. Expand
End-To-End Ego Lane Estimation Based on Sequential Transfer Learning for Self-Driving Cars
  • Jiman Kim, Chanjong Park
  • Computer Science
  • 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2017
A sequential end-to-end transfer learning method to estimate left and right ego lanes directly and separately without any postprocessing is proposed, which demonstrated improved accuracy and stability on input variations compared with a recent method based on deep learning. Expand
A Learning Approach Towards Detection and Tracking of Lane Markings
A pixel-hierarchy feature descriptor is proposed to model the contextual information shared by lane markings with the surrounding road region and a robust boosting algorithm to select relevant contextual features for detecting lane markings is proposed. Expand
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A new night-time lane detection system and its accompanying framework are presented in this paper, which is an improvement over the ALD 1.0 with integration of pixel remapping, outlier removal, and prediction with tracking. Expand
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A novel DVCNN strategy is designed where the front-view image and the top-view one are optimized simultaneously and a weighted hat-like filter which not only recalls potential lane line candidates, but also alleviates the disturbance of the gradual textures and reduces most false detections. Expand
Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene
A multitask deep convolutional network is developed, which simultaneously detects the presence of the target and the geometric attributes of thetarget with respect to the region of interest and a recurrent neuron layer is adopted for structured visual detection. Expand
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A unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions is proposed and achieves high accuracy and robustness under various conditions in realtime. Expand
Robust Lane Detection Based On Convolutional Neural Network and Random Sample Consensus
A robust lane detection method based on the combined convolutional neural network (CNN) with random sample consensus (RANSAC) algorithm is introduced and the performance is found to be better than other formal line detection algorithms such as RANSAC and hough transform. Expand
Robust lane markings detection and road geometry computation
This work adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation, which it claims addresses detection reliability better. Expand