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

• URL: http://arxiv.org/abs/2008.06204v1
• Date: Fri, 14 Aug 2020 06:28:20 GMT
• Title: Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor
• Authors: Wensheng Cheng, Hao Luo, Wen Yang, Lei Yu, Wei Li
• Abstract summary: We introduce Dynamic Vision Sensor (DVS), a type of event-based sensor to lane marker extraction task. We generate high-resolution DVS dataset for lane marker extraction with resolution of 1280$times$800 pixels. We then propose a structure-aware network for lane marker extraction in DVS images. We evaluate our proposed network with other state-of-the-art lane marker extraction models on this dataset.
• Score: 14.55881454495574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lane marker extraction is a basic yet necessary task for autonomous driving. Although past years have witnessed major advances in lane marker extraction with deep learning models, they all aim at ordinary RGB images generated by frame-based cameras, which limits their performance in extreme cases, like huge illumination change. To tackle this problem, we introduce Dynamic Vision Sensor (DVS), a type of event-based sensor to lane marker extraction task and build a high-resolution DVS dataset for lane marker extraction. We collect the raw event data and generate 5,424 DVS images with a resolution of 1280$\times$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.

Related papers

• PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting Network [24.54269823691119]
  We present an advanced study on more challenging high-resolution salient object detection (HRSOD) from both dataset and network framework perspectives. To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD. All the images are finely annotated in pixel-level, far exceeding previous low-resolution SOD datasets.
  arXiv  Detail & Related papers  (2024-08-02T09:31:21Z)
• DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition [51.96660522869841]
  DailyDVS-200 is a benchmark dataset tailored for the event-based action recognition community. It covers 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. DailyDVS-200 is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions.
  arXiv  Detail & Related papers  (2024-07-06T15:25:10Z)
• SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving [41.221988979184665]
  SUPS is a simulated dataset for underground automatic parking. It supports multiple tasks with multiple sensors and multiple semantic labels aligned with successive images. We also evaluate the state-of-the-art SLAM algorithms and perception models on our dataset.
  arXiv  Detail & Related papers  (2023-02-25T02:59:12Z)
• Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing [77.34726150561087]
  This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
  arXiv  Detail & Related papers  (2022-11-02T11:10:42Z)
• Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data [80.14669385741202]
  We propose a self-supervised pre-training method for 3D perception models tailored to autonomous driving data. We leverage the availability of synchronized and calibrated image and Lidar sensors in autonomous driving setups. Our method does not require any point cloud nor image annotations.
  arXiv  Detail & Related papers  (2022-03-30T12:40:30Z)
• Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation [32.33170182669095]
  This work investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars. We propose a novel method for cross-modal unsupervised learning of semantic image segmentation by leveraging synchronized LiDAR and image data.
  arXiv  Detail & Related papers  (2022-03-21T17:35:46Z)
• DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort [117.41383937100751]
  Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets. We show how the GAN latent code can be decoded to produce a semantic segmentation of the image. These generated datasets can then be used for training any computer vision architecture just as real datasets are.
  arXiv  Detail & Related papers  (2021-04-13T20:08:29Z)
• Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset [67.44497676652173]
  We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 $km2$, sampled from three Swiss cities. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras.
  arXiv  Detail & Related papers  (2020-12-23T21:48:47Z)
• TJU-DHD: A Diverse High-Resolution Dataset for Object Detection [48.94731638729273]
  Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods. We build a diverse high-resolution dataset (called TJU-DHD) The dataset contains 115,354 high-resolution images and 709,330 labeled objects with a large variance in scale and appearance.
  arXiv  Detail & Related papers  (2020-11-18T09:32:24Z)
• LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor [0.0]
  This paper explores the novel application of lane marking detection using an event camera. The spatial resolution of the encoded features is retained by a dense atrous spatial pyramid pooling block. The efficacy of the proposed work is evaluated using the DVS dataset for lane extraction.
  arXiv  Detail & Related papers  (2020-09-17T02:15:41Z)
• FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks [1.0062127381149395]
  This paper proposes a lane marking semantic segmentation method based on LIDAR and camera fusion deep neural network. Experiments on more than 14,000 image datasets have shown the proposed method has better performance on the semantic segmentation of the points cloud bird's eye view.
  arXiv  Detail & Related papers  (2020-03-09T20:33:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.