One-class anomaly detection through color-to-thermal AI for building envelope inspection

• URL: http://arxiv.org/abs/2402.02963v1
• Date: Mon, 5 Feb 2024 12:41:30 GMT
• Title: One-class anomaly detection through color-to-thermal AI for building envelope inspection
• Authors: Polina Kurtser, Kailun Feng, Thomas Olofsson, Aitor De Andres
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Effectively the method performs as a one-class classifier of the thermal image regions with high mismatch between the predicted and actual thermal distributions. The algorithm can learn to identify certain features as normal or anomalous by selecting the target sample used for training. We demonstrated this principle by training the algorithm with data collected at different outdoors temperature, which lead to the detection of thermal bridges. The method can be implemented to assist human professionals during routine building inspections or combined with mobile platforms for automating examination of large areas.

Related papers

• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Self-supervised learning for hotspot detection and isolation from thermal images [1.7132914341329852]
  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.
  arXiv  Detail & Related papers  (2023-08-25T06:59:26Z)
• Unsupervised Wildfire Change Detection based on Contrastive Learning [1.53934570513443]
  The accurate characterization of the severity of the wildfire event contributes to the characterization of the fuel conditions in fire-prone areas. The aim of this study is to develop an autonomous system built on top of high-resolution multispectral satellite imagery, with an advanced deep learning method for detecting burned area change.
  arXiv  Detail & Related papers  (2022-11-26T20:13:14Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• 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)
• Energy-Based Anomaly Detection and Localization [6.423239719448169]
  This brief sketches initial progress towards a unified energy-based solution for the visual anomaly detection and localization problem. We employ the density estimates from the energy-based model (EBM) as normalcy scores that can be used to discriminate normal images from anomalous ones. In addition to the spatial localization, we show that simple processing of the gradient map can also provide alternative normalcy scores.
  arXiv  Detail & Related papers  (2021-05-07T13:49:17Z)
• Robust pedestrian detection in thermal imagery using synthesized images [39.33977680993236]
  We propose a method for improving pedestrian detection in the thermal domain using two stages. First, a generative data augmentation approach is used, then a domain adaptation method using generated data adapts an RGB pedestrian detector. Our detector achieves the best single-modality detection results on KAIST with respect to the state-of-the-art.
  arXiv  Detail & Related papers  (2021-02-03T11:08:31Z)
• Unsupervised Dense Shape Correspondence using Heat Kernels [50.682560435495034]
  We propose an unsupervised method for learning dense correspondences between shapes using a recent deep functional map framework. Instead of depending on ground-truth correspondences or the computationally expensive geodesic distances, we use heat kernels. We present the results of our method on different benchmarks which have various challenges like partiality, topological noise and different connectivity.
  arXiv  Detail & Related papers  (2020-10-23T21:54:10Z)
• 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)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)

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.