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LDNet: End-to-End Lane Detection Approach usinga Dynamic Vision Sensor
Modern vehicles are equipped with various driver-assistance systems, inc...
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Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
Lane detection plays an important role in a self-driving vehicle. Severa...
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RESA: Recurrent Feature-Shift Aggregator for Lane Detection
Lane detection is one of the most important tasks in self-driving. Due t...
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Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks
The knowledge about the placement and appearance of lane markings is a p...
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EV-SegNet: Semantic Segmentation for Event-based Cameras
Event cameras, or Dynamic Vision Sensor (DVS), are very promising sensor...
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VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View
The automatic parking is being massively developed by car manufacturers ...
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APEX-Net: Automatic Plot Extractor Network
Automatic extraction of raw data from 2D line plot images is a problem o...
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Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor
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 lane marker extraction. We collect the raw event data and generate 5,424 DVS images with a resolution of 1280×800 pixels, the highest one among all DVS datasets available now. All images are annotated with multi-class semantic segmentation format. We then propose a structure-aware network for lane marker extraction in DVS images. It can capture directional information comprehensively with multidirectional slice convolution. We evaluate our proposed network with other state-of-the-art lane marker extraction models on this dataset. Experimental results demonstrate that our method outperforms other competitors. The dataset is made publicly available, including the raw event data, accumulated images and labels.
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