HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

• URL: http://arxiv.org/abs/2103.01849v1
• Date: Tue, 2 Mar 2021 16:35:05 GMT
• Title: HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
• Authors: Konrad Heidler, Lichao Mou, Celia Baumhoer, Andreas Dietz, Xiao Xiang Zhu
• Abstract summary: We devise a new model to unite two approaches to coastline detection in a deep learning model. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. An implementation of this approach is available at urlhttps://github.com/kr/HED-UNet.
• Score: 14.235722825330493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}.

Related papers

• A Deep Active Contour Model for Delineating Glacier Calving Fronts [17.061463565692456]
  Recent studies have shown that combining segmentation with edge detection can improve the accuracy of calving front detectors. We propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps. The proposed approach, called Charting Outlines by Recurrent Adaptation'' (COBRA), combines Convolutional Neural Networks (CNNs) for feature extraction and active contour models for the delineation.
  arXiv  Detail & Related papers  (2023-07-07T08:45:46Z)
• How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers [94.46825166907831]
  We present a training-free solution to tackle the object goal navigation problem in Embodied AI. Our method builds a structured scene representation based on the classic visual simultaneous localization and mapping (V-SLAM) framework. Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
  arXiv  Detail & Related papers  (2023-05-26T13:38:33Z)
• SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation [19.229255297016635]
  Self-supervised depth estimation can be trapped in a local minimum due to the gradient-locality issue of the photometric loss. We present a framework to enhance depth by leveraging semantic segmentation to guide the network to jump out of the local minimum.
  arXiv  Detail & Related papers  (2023-03-31T17:20:27Z)
• ALSO: Automotive Lidar Self-supervision by Occupancy estimation [70.70557577874155]
  We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled. The intuition is that if the network is able to reconstruct the scene surface, given only sparse input points, then it probably also captures some fragments of semantic information.
  arXiv  Detail & Related papers  (2022-12-12T13:10:19Z)
• Deep Shells: Unsupervised Shape Correspondence with Optimal Transport [52.646396621449]
  We propose a novel unsupervised learning approach to 3D shape correspondence. We show that the proposed method significantly improves over the state-of-the-art on multiple datasets.
  arXiv  Detail & Related papers  (2020-10-28T22:24:07Z)
• Cascade Network for Self-Supervised Monocular Depth Estimation [0.07161783472741746]
  We propose a new self-supervised learning method based on cascade networks. Compared with the previous self-supervised methods, our method has improved accuracy and reliability. We show a cascaded neural network that divides the target scene into parts of different sight distances and trains them separately to generate a better depth map.
  arXiv  Detail & Related papers  (2020-09-14T06:50:05Z)
• Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images [51.893494939675314]
  We propose a novel weakly supervised segmentation framework based on partial points annotation. We show that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-10T15:41:29Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)
• Seismic horizon detection with neural networks [62.997667081978825]
  This paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model. The main contribution of this paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model.
  arXiv  Detail & Related papers  (2020-01-10T11:30:50Z)

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.