Self-supervised learning for hotspot detection and isolation from thermal images

• URL: http://arxiv.org/abs/2308.13204v1
• Date: Fri, 25 Aug 2023 06:59:26 GMT
• Title: Self-supervised learning for hotspot detection and isolation from thermal images
• Authors: Shreyas Goyal, Jagath C. Rajapakse
• Abstract summary: We address the problem of hotspot detection in thermal images by proposing a self-supervised learning approach. We create a novel large thermal image dataset to address the issue of paucity of easily accessible thermal images. We achieve a Dice Coefficient of 0.736, the highest when compared with existing hotspot identification techniques.
• Score: 1.7132914341329852
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hotspot detection using thermal imaging has recently become essential in several industrial applications, such as security applications, health applications, and equipment monitoring applications. Hotspot detection is of utmost importance in industrial safety where equipment can develop anomalies. Hotspots are early indicators of such anomalies. We address the problem of hotspot detection in thermal images by proposing a self-supervised learning approach. Self-supervised learning has shown potential as a competitive alternative to their supervised learning counterparts but their application to thermography has been limited. This has been due to lack of diverse data availability, domain specific pre-trained models, standardized benchmarks, etc. We propose a self-supervised representation learning approach followed by fine-tuning that improves detection of hotspots by classification. The SimSiam network based ensemble classifier decides whether an image contains hotspots or not. Detection of hotspots is followed by precise hotspot isolation. By doing so, we are able to provide a highly accurate and precise hotspot identification, applicable to a wide range of applications. We created a novel large thermal image dataset to address the issue of paucity of easily accessible thermal images. Our experiments with the dataset created by us and a publicly available segmentation dataset show the potential of our approach for hotspot detection and its ability to isolate hotspots with high accuracy. We achieve a Dice Coefficient of 0.736, the highest when compared with existing hotspot identification techniques. Our experiments also show self-supervised learning as a strong contender of supervised learning, providing competitive metrics for hotspot detection, with the highest accuracy of our approach being 97%.

Related papers

• Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data [0.12289361708127873]
  Leveraging remote sensed data from satellite networks and advanced AI models to automatically detect hotspots is an effective way to build wildfire monitoring systems. We propose a novel dataset containing time series of remotely sensed data related to European fire events and a Self-Supervised Learning (SSL)-based model able to analyse multi-temporal data and identify hotspots in potentially near real time. We train and evaluate the performance of our model using our dataset and Thraws, a dataset of thermal anomalies including several fire events, obtaining an F1 score of 63.58.
  arXiv  Detail & Related papers  (2024-05-30T14:31:46Z)
• One-class anomaly detection through color-to-thermal AI for building envelope inspection [0.0]
  We present a label-free method for detecting anomalies during thermographic inspection of building envelopes. It is based on the AI-driven prediction of thermal distributions from color images.
  arXiv  Detail & Related papers  (2024-02-05T12:41:30Z)
• A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe [0.34410212782758043]
  We propose a wildfire identification solution to improve the accuracy of automated satellite-based hotspot detection systems. We cross-reference the thermal anomalies detected by the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) hotspot services. Then, we propose a novel supervised machine learning approach to disambiguate hotspot detections, distinguishing between wildfires and other events.
  arXiv  Detail & Related papers  (2023-07-27T08:28:57Z)
• Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning [62.18197846270103]
  Current point-cloud detection methods have difficulty detecting the open-vocabulary objects in the real world. We propose OV-3DETIC, an Open-Vocabulary 3D DETector using Image-level Class supervision.
  arXiv  Detail & Related papers  (2022-07-05T12:13:52Z)
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [65.30079184700755]
  This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network.
  arXiv  Detail & Related papers  (2022-03-30T11:44:56Z)
• Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning [64.92447072894055]
  Infrared (IR) cameras are robust under adverse illumination and lighting conditions. We propose an algorithm meta-learning framework to improve existing UDA methods. We produce a state-of-the-art thermal detector for the KAIST and DSIAC datasets.
  arXiv  Detail & Related papers  (2021-10-07T02:28:18Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• FiSH: Fair Spatial Hotspots [7.472488862351863]
  We consider, for the first time, fairness in detecting spatial hot spots. We characterize the task of identifying a diverse set of solutions in the noteworthiness-fairness trade-off spectrum. We devise a method, codenamed it FiSH, for efficiently identifying high-quality, fair and diverse sets of spatial hot spots.
  arXiv  Detail & Related papers  (2021-06-01T10:29:03Z)
• Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data [64.40187171234838]
  Seasonal Contrast (SeCo) is an effective pipeline to leverage unlabeled data for in-domain pre-training of re-mote sensing representations. SeCo will be made public to facilitate transfer learning and enable rapid progress in re-mote sensing applications.
  arXiv  Detail & Related papers  (2021-03-30T18:26:39Z)
• Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving [67.69430435482127]
  Underexposure regions are vital to construct a complete perception of the surroundings for safe autonomous driving. The availability of thermal cameras has provided an essential alternate to explore regions where other optical sensors lack in capturing interpretable signals. This work proposes a domain adaptation framework which employs a style transfer technique for transfer learning from visible spectrum images to thermal images.
  arXiv  Detail & Related papers  (2020-06-01T09:59:09Z)

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.