PointCrack3D: Crack Detection in Unstructured Environments using a 3D-Point-Cloud-Based Deep Neural Network

• URL: http://arxiv.org/abs/2111.11615v1
• Date: Tue, 23 Nov 2021 02:33:18 GMT
• Title: PointCrack3D: Crack Detection in Unstructured Environments using a 3D-Point-Cloud-Based Deep Neural Network
• Authors: Faris Azhari and Charlotte Sennersten and Michael Milford and Thierry Peynot
• Abstract summary: This paper presents PointCrack3D, a new 3D-point-cloud-based crack detection algorithm for unstructured surfaces. The method was validated experimentally on a new large natural rock dataset. Results demonstrate a crack detection rate of 97% overall and 100% for cracks with a maximum width of more than 3 cm.
• Score: 20.330700719146215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Surface cracks on buildings, natural walls and underground mine tunnels can indicate serious structural integrity issues that threaten the safety of the structure and people in the environment. Timely detection and monitoring of cracks are crucial to managing these risks, especially if the systems can be made highly automated through robots. Vision-based crack detection algorithms using deep neural networks have exhibited promise for structured surfaces such as walls or civil engineering tunnels, but little work has addressed highly unstructured environments such as rock cliffs and bare mining tunnels. To address this challenge, this paper presents PointCrack3D, a new 3D-point-cloud-based crack detection algorithm for unstructured surfaces. The method comprises three key components: an adaptive down-sampling method that maintains sufficient crack point density, a DNN that classifies each point as crack or non-crack, and a post-processing clustering method that groups crack points into crack instances. The method was validated experimentally on a new large natural rock dataset, comprising coloured LIDAR point clouds spanning more than 900 m^2 and 412 individual cracks. Results demonstrate a crack detection rate of 97% overall and 100% for cracks with a maximum width of more than 3 cm, significantly outperforming the state of the art. Furthermore, for cross-validation, PointCrack3D was applied to an entirely new dataset acquired in different locations and not used at all in training and shown to detect 100% of its crack instances. We also characterise the relationship between detection performance, crack width and number of points per crack, providing a foundation upon which to make decisions about both practical deployments and future research directions.

Related papers

• Deep Learning for Micro-Scale Crack Detection on Imbalanced Datasets Using Key Point Localization [1.0136215038345013]
  Internal crack detection has been a subject of focus in structural health monitoring. It is demonstrated that deep learning (DL) methods can effectively analyze seismic wave fields interacting with micro-scale cracks. This work explores a novel application of DL-based key point detection technique, where cracks are localized by predicting the coordinates of four key points that define a bounding region of the crack.
  arXiv  Detail & Related papers  (2024-11-15T17:50:46Z)
• Deep Learning-Based Fatigue Cracks Detection in Bridge Girders using Feature Pyramid Networks [8.59780173800845]
  This study proposes a framework of automatic crack segmentation from high-resolution images containing crack information about steel box girders of bridges. Considering the multi-scale feature of cracks, convolutional neural network architecture of Feature Pyramid Networks (FPN) for crack detection is proposed.
  arXiv  Detail & Related papers  (2024-10-28T16:16:15Z)
• EfficientCrackNet: A Lightweight Model for Crack Segmentation [1.3689715712707347]
  Crack detection is crucial for maintaining the structural integrity of buildings, pavements, and bridges. Existing lightweight methods often face challenges including computational inefficiency, complex crack patterns, and difficult backgrounds. We propose EfficientCrackNet, a lightweight hybrid model combining Convolutional Neural Networks (CNNs) and transformers for precise crack segmentation.
  arXiv  Detail & Related papers  (2024-09-26T17:44:20Z)
• Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure [52.2025114590481]
  We introduce Hybrid-Segmentor, an encoder-decoder based approach that is capable of extracting both fine-grained local and global crack features. This allows the model to improve its generalization capabilities in distinguish various type of shapes, surfaces and sizes of cracks. The proposed model outperforms existing benchmark models across 5 quantitative metrics (accuracy 0.971, precision 0.804, recall 0.744, F1-score 0.770, and IoU score 0.630), achieving state-of-the-art status.
  arXiv  Detail & Related papers  (2024-09-04T16:47:16Z)
• Clustering based Point Cloud Representation Learning for 3D Analysis [80.88995099442374]
  We propose a clustering based supervised learning scheme for point cloud analysis. Unlike current de-facto, scene-wise training paradigm, our algorithm conducts within-class clustering on the point embedding space. Our algorithm shows notable improvements on famous point cloud segmentation datasets.
  arXiv  Detail & Related papers  (2023-07-27T03:42:12Z)
• Robotic surface exploration with vision and tactile sensing for cracks detection and characterisation [7.627217550282436]
  This paper presents a novel algorithm for crack localisation and detection based on visual and tactile analysis via fibre-optics. A finger-shaped sensor based on fibre-optics is employed for the data acquisition to collect data for the analysis and experiments.
  arXiv  Detail & Related papers  (2023-07-13T14:50:38Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• 3D Cascade RCNN: High Quality Object Detection in Point Clouds [122.42455210196262]
  We present 3D Cascade RCNN, which allocates multiple detectors based on the voxelized point clouds in a cascade paradigm. We validate the superiority of our proposed 3D Cascade RCNN, when comparing to state-of-the-art 3D object detection techniques.
  arXiv  Detail & Related papers  (2022-11-15T15:58:36Z)
• Semi-signed neural fitting for surface reconstruction from unoriented point clouds [53.379712818791894]
  We propose SSN-Fitting to reconstruct a better signed distance field. SSN-Fitting consists of a semi-signed supervision and a loss-based region sampling strategy. We conduct experiments to demonstrate that SSN-Fitting achieves state-of-the-art performance under different settings.
  arXiv  Detail & Related papers  (2022-06-14T09:40:17Z)
• Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations [68.0524382279567]
  We present a robust learning algorithm to detect and handle collisions in 3D deforming meshes. Our approach outperforms supervised learning methods and achieves $93.8-98.1%$ accuracy.
  arXiv  Detail & Related papers  (2021-10-08T04:08:31Z)
• 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)

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.