LineMarkNet: Line Landmark Detection for Valet Parking

• URL: http://arxiv.org/abs/2309.10475v2
• Date: Mon, 25 Sep 2023 03:39:34 GMT
• Title: LineMarkNet: Line Landmark Detection for Valet Parking
• Authors: Zizhang Wu, Yuanzhu Gan, Tianhao Xu, Rui Tang and Jian Pu
• Abstract summary: We develop a deep network (LineMarkNet) to detect line landmarks from surround-view cameras. We then employ the multi-task decoder to detect multiple line landmarks. Experimental results show that our framework achieves the enhanced performance compared with several line detection methods.
• Score: 13.563702256927135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We aim for accurate and efficient line landmark detection for valet parking, which is a long-standing yet unsolved problem in autonomous driving. To this end, we present a deep line landmark detection system where we carefully design the modules to be lightweight. Specifically, we first empirically design four general line landmarks including three physical lines and one novel mental line. The four line landmarks are effective for valet parking. We then develop a deep network (LineMarkNet) to detect line landmarks from surround-view cameras where we, via the pre-calibrated homography, fuse context from four separate cameras into the unified bird-eye-view (BEV) space, specifically we fuse the surroundview features and BEV features, then employ the multi-task decoder to detect multiple line landmarks where we apply the center-based strategy for object detection task, and design our graph transformer to enhance the vision transformer with hierarchical level graph reasoning for semantic segmentation task. At last, we further parameterize the detected line landmarks (e.g., intercept-slope form) whereby a novel filtering backend incorporates temporal and multi-view consistency to achieve smooth and stable detection. Moreover, we annotate a large-scale dataset to validate our method. Experimental results show that our framework achieves the enhanced performance compared with several line detection methods and validate the multi-task network's efficiency about the real-time line landmark detection on the Qualcomm 820A platform while meantime keeps superior accuracy, with our deep line landmark detection system.

Related papers

• RoadPainter: Points Are Ideal Navigators for Topology transformER [10.179711440042123]
  Topology reasoning aims to provide a precise understanding of road scenes, enabling autonomous systems to identify safe and efficient routes. We present RoadPainter, an innovative approach for detecting and reasoning the topology of lane centerlines using multi-view images.
  arXiv  Detail & Related papers  (2024-07-22T03:23:35Z)
• Infinite 3D Landmarks: Improving Continuous 2D Facial Landmark Detection [9.633565294243173]
  We show how a combination of specific architectural modifications can improve their accuracy and temporal stability. We analyze the use of a spatial transformer network that is trained alongside the landmark detector in an unsupervised manner. We show that modifying the output head of the landmark predictor to infer landmarks in a canonical 3D space can further improve accuracy.
  arXiv  Detail & Related papers  (2024-05-30T14:54:26Z)
• DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients [105.25109274550607]
  Line segments are increasingly used in vision tasks. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector.
  arXiv  Detail & Related papers  (2022-12-15T12:36:49Z)
• RCLane: Relay Chain Prediction for Lane Detection [76.62424079494285]
  We present a new method for lane detection based on relay chain prediction. Our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.
  arXiv  Detail & Related papers  (2022-07-19T16:48:39Z)
• From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery [37.78933209094847]
  This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors. We validate our method on a variety of difficult datasets, including LS3D, BBCPose, Human3.6M and PennAction.
  arXiv  Detail & Related papers  (2022-05-31T15:44:29Z)
• SOLD2: Self-supervised Occlusion-aware Line Description and Detection [95.8719432775724]
  We introduce the first joint detection and description of line segments in a single deep network. Our method does not require any annotated line labels and can therefore generalize to any dataset. We evaluate our approach against previous line detection and description methods on several multi-view datasets.
  arXiv  Detail & Related papers  (2021-04-07T19:27:17Z)
• Pretrained equivariant features improve unsupervised landmark discovery [69.02115180674885]
  We formulate a two-step unsupervised approach that overcomes this challenge by first learning powerful pixel-based features. Our method produces state-of-the-art results in several challenging landmark detection datasets.
  arXiv  Detail & Related papers  (2021-04-07T05:42:11Z)
• Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving [11.576868193291997]
  We propose a new benchmark dataset, named textitTopo-boundary, for off-line topological road-boundary detection. The dataset contains 21,556 $1000times1000$-sized 4-channel aerial images. We implement and evaluate 3 segmentation-based baselines and 5 graph-based baselines using the dataset.
  arXiv  Detail & Related papers  (2021-03-31T14:42:00Z)
• Deep Hough Transform for Semantic Line Detection [70.28969017874587]
  We focus on a fundamental task of detecting meaningful line structures, a.k.a. semantic lines, in natural scenes. Previous methods neglect the inherent characteristics of lines, leading to sub-optimal performance. We propose a one-shot end-to-end learning framework for line detection.
  arXiv  Detail & Related papers  (2020-03-10T13:08:42Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

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.