Detecting Extratropical Cyclones of the Northern Hemisphere with Single Shot Detector

• URL: http://arxiv.org/abs/2112.01283v1
• Date: Wed, 1 Dec 2021 00:46:37 GMT
• Title: Detecting Extratropical Cyclones of the Northern Hemisphere with Single Shot Detector
• Authors: Minjing Shi, Pengfei He, Yuli Shi
• Abstract summary: We propose a deep learning-based model to detect extratropical cyclones (ETCs) of northern hemisphere. We first label the cyclone center by adapting an approach from Bonfanti et.al. We then propose a framework of labeling and preprocessing the images in our dataset.
• Score: 1.4502611532302039
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we propose a deep learning-based model to detect extratropical cyclones (ETCs) of northern hemisphere, while developing a novel workflow of processing images and generating labels for ETCs. We first label the cyclone center by adapting an approach from Bonfanti et.al. [1] and set up criteria of labeling ETCs of three categories: developing, mature, and declining stages. We then propose a framework of labeling and preprocessing the images in our dataset. Once the images and labels are ready to serve as inputs, we create our object detection model named Single Shot Detector (SSD) to fit the format of our dataset. We train and evaluate our model with our labeled dataset on two settings (binary and multiclass classifications), while keeping a record of the results. Finally, we achieved relatively high performance with detecting ETCs of mature stage (mean Average Precision is 86.64%), and an acceptable result for detecting ETCs of all three categories (mean Average Precision 79.34%). We conclude that the single-shot detector model can succeed in detecting ETCs of different stages, and it has demonstrated great potential in the future applications of ETC detection in other relevant settings.

Related papers

• Self-supervised co-salient object detection via feature correspondence at multiple scales [27.664016341526988]
  This paper introduces a novel two-stage self-supervised approach for detecting co-occurring salient objects (CoSOD) in image groups without requiring segmentation annotations. We train a self-supervised network that detects co-salient regions by computing local patch-level feature correspondences across images. In experiments on three CoSOD benchmark datasets, our model outperforms the corresponding state-of-the-art models by a huge margin.
  arXiv  Detail & Related papers  (2024-03-17T06:21:21Z)
• Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning [50.809769498312434]
  We propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS) Our method achieves 54.51% accuracy with only 10% training data, surpassing random selection by 7.83% and other comparison methods by at least 12.69%.
  arXiv  Detail & Related papers  (2023-11-22T03:45:30Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data [0.0]
  Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Traditionally, TCs have been identified in large climate datasets through the use of deterministic tracking schemes. This study presents a Machine Learning ensemble approach for locating TC center coordinates.
  arXiv  Detail & Related papers  (2023-06-07T22:26:50Z)
• Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic Labels [1.534667887016089]
  This work proposes a hybrid unsupervised and supervised learning method to pre-train models applied in Earth observation downstream tasks. We combine a contrastive approach to pre-train models with a pixel-wise regression pre-text task to predict coarse elevation maps.
  arXiv  Detail & Related papers  (2023-04-13T23:01:11Z)
• Revisiting the Evaluation of Image Synthesis with GANs [55.72247435112475]
  This study presents an empirical investigation into the evaluation of synthesis performance, with generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses of various factors, including how to represent a data point in the representation space, how to calculate a fair distance using selected samples, and how many instances to use from each set.
  arXiv  Detail & Related papers  (2023-04-04T17:54:32Z)
• U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold [0.40964539027092906]
  We propose a one-class self-supervised method for anomaly segmentation in images. It benefits from a modern machine learning approach and a more classic statistical detection theory. The proposed approach produces state-of-the-art results for all metrics and all datasets.
  arXiv  Detail & Related papers  (2022-11-22T15:43:19Z)
• 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection [76.42897462051067]
  3DIoUMatch is a novel semi-supervised method for 3D object detection applicable to both indoor and outdoor scenes. We leverage a teacher-student mutual learning framework to propagate information from the labeled to the unlabeled train set in the form of pseudo-labels. Our method consistently improves state-of-the-art methods on both ScanNet and SUN-RGBD benchmarks by significant margins under all label ratios.
  arXiv  Detail & Related papers  (2020-12-08T11:06:26Z)
• 3DSSD: Point-based 3D Single Stage Object Detector [61.67928229961813]
  We present a point-based 3D single stage object detector, named 3DSSD, achieving a good balance between accuracy and efficiency. Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well.
  arXiv  Detail & Related papers  (2020-02-24T12:01:58Z)
• EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement [53.69674636044927]
  We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
  arXiv  Detail & Related papers  (2020-02-18T08:04:58Z)

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.