ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection

• URL: http://arxiv.org/abs/2401.03298v2
• Date: Wed, 02 Oct 2024 20:24:14 GMT
• Title: ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection
• Authors: Christian Benz, Volker Rodehorst,
• Abstract summary: ENSTRECT is a stage-based approach designed to accomplish 2.5D structural damage detection. With a localization tolerance of 4cm, ENSTRECT can achieve IoUs of over 90% for cracks, 82% for corrosion, and 41% for spalling.
• Score: 0.0
• License:
• Abstract: To effectively assess structural damage, it is essential to localize the instances of damage in the physical world of a civil structure. ENSTRECT is a stage-based approach designed to accomplish 2.5D structural damage detection. The method requires an image collection, the relative orientation, and a point cloud. Using these inputs, surface damages are segmented at the image level and then mapped into the point cloud space, resulting in a segmented point cloud. To enable further quantitative analyses, the segmented point cloud is transformed into measurable damage instances: cracks are extracted by contracting the clustered point cloud into a corresponding medial axis. For areal damages, such as spalling and corrosion, a procedure is proposed to compute the bounding polygon based on PCA and alpha shapes. With a localization tolerance of 4cm, ENSTRECT can achieve IoUs of over 90% for cracks, 82% for corrosion, and 41% for spalling. Detection at the instance level yields an AP50 of about 45% (cracks, spalling) and 56% (corrosion).

Related papers

• CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery [0.5699788926464749]
  CRASAR-U-DROIDs is the largest labeled dataset of sUAS orthomosaic imagery. The CRASAR-U-DRIODs dataset consists of fifty-two (52) orthomosaics from ten (10) federally declared disasters.
  arXiv  Detail & Related papers  (2024-07-24T23:39:10Z)
• Pavement Fatigue Crack Detection and Severity Classification Based on Convolutional Neural Network [8.233892677749276]
  Fatigue cracking, also known as alligator cracking, is one of the common distresses of asphalt pavement. A novel deep convolutional neural network that can achieve two objectives is proposed.
  arXiv  Detail & Related papers  (2024-07-22T19:56:03Z)
• Visual Context-Aware Person Fall Detection [52.49277799455569]
  We present a segmentation pipeline to semi-automatically separate individuals and objects in images. Background objects such as beds, chairs, or wheelchairs can challenge fall detection systems, leading to false positive alarms. We demonstrate that object-specific contextual transformations during training effectively mitigate this challenge.
  arXiv  Detail & Related papers  (2024-04-11T19:06:36Z)
• Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge [66.86170104167608]
  The RibFrac Challenge provides a benchmark dataset of over 5,000 rib fractures from 660 CT scans. During the MICCAI 2020 challenge period, 243 results were evaluated, and seven teams were invited to participate in the challenge summary. The analysis revealed that several top rib fracture detection solutions achieved performance comparable or even better than human experts.
  arXiv  Detail & Related papers  (2024-02-14T18:18:33Z)
• Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data [58.720142291102135]
  We present a novel approach to automatically assess multi-class building damage from real-world point clouds. We use a machine learning model trained on virtual laser scanning (VLS) data. The model yields high multi-target classification accuracies (overall accuracy: 92.0% - 95.1%)
  arXiv  Detail & Related papers  (2023-02-24T12:04:46Z)
• PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples [63.84378007819262]
  We propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones. We show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01.
  arXiv  Detail & Related papers  (2022-11-22T14:15:41Z)
• Detection Of Concrete Cracks using Dual-channel Deep Convolutional Network [0.0]
  This study proposes a crack detection method based on deep convolutional neural network (CNN) for detection of concrete cracks. A database of 3200 labelled images with concrete cracks has been created, where the contrast, lighting conditions, orientations and severity of the cracks were extremely variable. We have designed a dual-channel deep CNN which shows high accuracy ( 92.25%) as well as robustness in finding concrete cracks in realis-tic situations.
  arXiv  Detail & Related papers  (2020-09-22T15:17:02Z)
• Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge [0.0]
  We propose a synthetic augmentation procedure to generate damaged images using the image-to-image translation mapping. We apply popular per-pixel segmentation algorithms such as the FCN-8s, SegNet, and DeepLabv3+Xception-v2. We demonstrate that re-training a data set added with synthetic augmentation procedure make higher accuracy based on indices the mean IoU, damage region of interest IoU, precision, recall, BF score when we predict test images.
  arXiv  Detail & Related papers  (2020-05-07T06:04:02Z)
• RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery [83.49145695899388]
  RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
  arXiv  Detail & Related papers  (2020-04-15T19:52:09Z)
• Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images [69.74667930907314]
  We propose a bending loss regularized network for nuclei segmentation. The proposed bending loss defines high penalties to contour points with large curvatures, and applies small penalties to contour points with small curvature. Minimizing the bending loss can avoid generating contours that encompass multiple nuclei.
  arXiv  Detail & Related papers  (2020-02-03T21:20:50Z)

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.