Robotic surface exploration with vision and tactile sensing for cracks detection and characterisation

• URL: http://arxiv.org/abs/2307.06784v1
• Date: Thu, 13 Jul 2023 14:50:38 GMT
• Title: Robotic surface exploration with vision and tactile sensing for cracks detection and characterisation
• Authors: Francesca Palermo, Bukeikhan Omarali, Changae Oh, Kaspar Althoefer, Ildar Farkhatdinov
• Abstract summary: 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.
• Score: 7.627217550282436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 the experiments. To detect the possible locations of cracks a camera is used to scan an environment while running an object detection algorithm. Once the crack is detected, a fully-connected graph is created from a skeletonised version of the crack. A minimum spanning tree is then employed for calculating the shortest path to explore the crack which is then used to develop the motion planner for the robotic manipulator. The motion planner divides the crack into multiple nodes which are then explored individually. Then, the manipulator starts the exploration and performs the tactile data classification to confirm if there is indeed a crack in that location or just a false positive from the vision algorithm. If a crack is detected, also the length, width, orientation and number of branches are calculated. This is repeated until all the nodes of the crack are explored. In order to validate the complete algorithm, various experiments are performed: comparison of exploration of cracks through full scan and motion planning algorithm, implementation of frequency-based features for crack classification and geometry analysis using a combination of vision and tactile data. From the results of the experiments, it is shown that the proposed algorithm is able to detect cracks and improve the results obtained from vision to correctly classify cracks and their geometry with minimal cost thanks to the motion planning algorithm.

Related papers

• Segmentation tool for images of cracks [0.16492989697868887]
  This paper proposes a semi-automatic crack segmentation tool that eases the manual segmentation of cracks on images. Also, it can be used to measure the geometry of the crack. The proposed method outperforms fully automatic methods and shows potential to be an adequate alternative to the manual data annotation.
  arXiv  Detail & Related papers  (2024-03-28T15:23:52Z)
• Model Pairing Using Embedding Translation for Backdoor Attack Detection on Open-Set Classification Tasks [63.269788236474234]
  We propose to use model pairs on open-set classification tasks for detecting backdoors. We show that this score, can be an indicator for the presence of a backdoor despite models being of different architectures. This technique allows for the detection of backdoors on models designed for open-set classification tasks, which is little studied in the literature.
  arXiv  Detail & Related papers  (2024-02-28T21:29:16Z)
• Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding [51.38395069380457]
  Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability. In this study, we specifically explore the use of shape auto-encoders for vertebrae.
  arXiv  Detail & Related papers  (2023-12-08T18:11:22Z)
• Unsupervised crack detection on complex stone masonry surfaces [0.0]
  In this article a crack detection methodology for stone masonry walls is presented. In the proposed approach, crack detection is approached as an unsupervised anomaly detection problem on RGB (Red Green Blue) image patches.
  arXiv  Detail & Related papers  (2023-03-31T12:07:23Z)
• Real-time AdaBoost cascade face tracker based on likelihood map and optical flow [59.17685450892182]
  The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered. The proposed tracker preserves information about the number of classification stages passed by each window.
  arXiv  Detail & Related papers  (2022-10-25T10:15:07Z)
• What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification [0.9449650062296824]
  This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting. The review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms.
  arXiv  Detail & Related papers  (2022-02-08T08:22:26Z)
• PointCrack3D: Crack Detection in Unstructured Environments using a 3D-Point-Cloud-Based Deep Neural Network [20.330700719146215]
  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.
  arXiv  Detail & Related papers  (2021-11-23T02:33:18Z)
• Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
  We design a series of forgery detection systems that each focus on one individual part of the face. We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
  arXiv  Detail & Related papers  (2021-09-21T16:18:45Z)
• Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search [101.73248560009124]
  We propose an effective convergence-simulation driven evolutionary search algorithm, CSE-Autoloss, for speeding up the search progress. We conduct extensive evaluations of loss function search on popular detectors and validate the good generalization capability of searched losses. Our experiments show that the best-discovered loss function combinations outperform default combinations by 1.1% and 0.8% in terms of mAP for two-stage and one-stage detectors.
  arXiv  Detail & Related papers  (2021-02-09T08:34:52Z)
• AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning [72.99415402575886]
  Outlier detection is an important data mining task with numerous practical applications. We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
  arXiv  Detail & Related papers  (2020-06-19T18:57:51Z)
• Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement [9.34360241512198]
  An ensemble of convolutional neural networks was employed to identify the structure of small cracks. For crack measurement, the crack length and width can be measure based on different crack types.
  arXiv  Detail & Related papers  (2020-02-08T22:15:11Z)

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.