Fast and Robust Structural Damage Analysis of Civil Infrastructure Using UAV Imagery

• URL: http://arxiv.org/abs/2110.04806v1
• Date: Sun, 10 Oct 2021 14:24:26 GMT
• Title: Fast and Robust Structural Damage Analysis of Civil Infrastructure Using UAV Imagery
• Authors: Alon Oring
• Abstract summary: We propose an end-to-end method for automated structural inspection damage analysis. Using automated object detection and segmentation we accurately localize defects, bridge utilities and elements. Our technique not only enables fast and robust damage analysis of UAV imagery, as we show herein, but is also effective for analyzing manually acquired images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The usage of Unmanned Aerial Vehicles (UAVs) in the context of structural health inspection is recently gaining tremendous popularity. Camera mounted UAVs enable the fast acquisition of a large number of images often used for mapping, 3D model reconstruction, and as an assisting tool for inspectors. Due to the number of images captured during large scale UAV surveys, a manual image-based inspection analysis of entire assets cannot be efficiently performed by qualified engineers. Additionally, comparing defects to past inspections requires the retrieval of relevant images which is often impractical without extensive metadata or computer-vision-based algorithms. In this paper, we propose an end-to-end method for automated structural inspection damage analysis. Using automated object detection and segmentation we accurately localize defects, bridge utilities and elements. Next, given the high overlap in UAV imagery, points of interest are extracted, and defects are located and matched throughout the image database, considerably reducing data redundancy while maintaining a detailed record of the defects. Our technique not only enables fast and robust damage analysis of UAV imagery, as we show herein, but is also effective for analyzing manually acquired images.

Related papers

• UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection [0.03464344220266879]
  Patch Intensity Convergence (PIC) technique generates high-fidelity bounding boxes for UAV detection without manual labeling. This technique forms the foundation of UAVDB, a dedicated database designed specifically for UAV detection. We benchmark UAVDB using state-of-the-art (SOTA) YOLO series detectors, providing a comprehensive performance analysis.
  arXiv  Detail & Related papers  (2024-09-09T13:27:53Z)
• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage [1.8923948104852863]
  We develop a semantic segmentation network based on vision transformers and Laplacian pyramids scaling networks. The proposed framework has been evaluated through comprehensive experiments on a dataset of bridge inspection report images.
  arXiv  Detail & Related papers  (2023-08-06T03:34:25Z)
• High-Fidelity Visual Structural Inspections through Transformers and Learnable Resizers [2.126862120884775]
  Recent advances in unmanned aerial vehicles (UAVs) and artificial intelligence have made the visual inspections faster, safer, and more reliable. High-resolution segmentation is extremely challenging due to the high computational memory demands. We propose a hybrid strategy that can adapt to different inspections tasks by managing the global and local semantics trade-off.
  arXiv  Detail & Related papers  (2022-10-21T18:08:26Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• Test-time Adaptation with Slot-Centric Models [63.981055778098444]
  Slot-TTA is a semi-supervised scene decomposition model that at test time is adapted per scene through gradient descent on reconstruction or cross-view synthesis objectives. We show substantial out-of-distribution performance improvements against state-of-the-art supervised feed-forward detectors, and alternative test-time adaptation methods.
  arXiv  Detail & Related papers  (2022-03-21T17:59:50Z)
• Validation of object detection in UAV-based images using synthetic data [9.189702268557483]
  Machine learning (ML) models for UAV-based detection are often validated using data curated for tasks unrelated to the UAV application. Such errors arise due to differences in imaging conditions between images from UAVs and images in training. Our work is focused on understanding the impact of different UAV-based imaging conditions on detection performance by using synthetic data generated using a game engine.
  arXiv  Detail & Related papers  (2022-01-17T20:56:56Z)
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization [59.719925639875036]
  We propose a framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects.
  arXiv  Detail & Related papers  (2021-04-08T19:04:55Z)
• Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection [1.4528189330418975]
  We propose a generative approach that generates top-down images by overlaying artificial vehicles created from 2D CAD drawings on artificial or real backgrounds. Our experiments with a modified RetinaNet object detection network show that adding these images to small real-world datasets significantly improves detection performance.
  arXiv  Detail & Related papers  (2021-04-07T11:06:15Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module [50.75589128518707]
  We contribute the first high-quality object detection dataset for security inspection, named OPIXray. OPIXray focused on the widely-occurred prohibited item "cutter", annotated manually by professional inspectors from the international airport. We propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors.
  arXiv  Detail & Related papers  (2020-04-18T16:10:55Z)

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.