Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies

Jiang, Yuhan; Han, Sisi; Bai, Yong

doi:10.1061/(asce)cf.1943-5509.0001652

Cited by 28 publications

(9 citation statements)

References 38 publications

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“…Eguchi et al 98 transformed the 3D point cloud data of the pavement into color information images, and used the AlexNet model to detect the spots on the asphalt surface. In order to obtain the detection effect of the optimal 3D point cloud image, Jiang et al 78 compared the performance of RGB, depth and normal vectors and their combined feature images from photogrammetry point clouds for image segmentation tasks on the U-Net model. Experiments showed that the optimal effect can be achieved in the combined feature image of depth and normal vector.…”

Section: Based On Deep Learningmentioning

confidence: 99%

“…In places where light is not fully illuminated, this method is difficult to play a role. 78 Therefore, Methods based on 3D point cloud images are proposed, whose core idea is to learn the region of interest features on 2D depth images mapped into 3D point cloud. 79,80 These methods do not need to consider the external lighting conditions, shooting location, and the disorderly nature of 3D point cloud, and can play a better suppression for the interference of outliers, but this method needs to establish the point-image mapping relationship between 2D images and point cloud.…”

Section: Point Cloud Modal Transformationmentioning

confidence: 99%

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Review: Research on product surface quality inspection technology based on 3D point cloud

Huo

Liu

Yang

et al. 2023

Advances in Mechanical Engineering

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In recent years, machine vision has played an important role in product surface quality detection. The promotion and use of this technology have largely avoided the subjectivity caused by human detection and improved detection efficiency and accuracy. Different from the image data commonly used in machine vision, point cloud can describe the spatial structure of an object, provide more information than image data, and have the ability to expand the data to build multi-dimensional data models. Due to the strong anti-interference ability of point cloud sensors and the high accuracy of three-dimensional positioning information point cloud, nondestructive testing technology based on point cloud has received more and more attention. This paper summarizes the research progress of product surface quality detection methods based on 3D point cloud in recent years. According to different data processing methods, the detection research is divided into five categories: based on point cloud contour, based on local geometric feature, based on template matching, based on multimodal point cloud, and based on deep learning. The five methods are reviewed and summarized respectively. Finally, the key problems of each detection method and the future trend of product surface quality detection technology based on 3D point cloud are discussed.

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Section: Based On Deep Learningmentioning

confidence: 99%

Section: Point Cloud Modal Transformationmentioning

confidence: 99%

Review: Research on product surface quality inspection technology based on 3D point cloud

Huo

Liu

Yang

et al. 2023

Advances in Mechanical Engineering

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“…The last few years have also seen various DL-based crack detection studies using 3D images [222,223]. The state-of-the-art DL approaches can also be improved to adapt the 3D imaging technologies to improve model generalization ability and consider new crack features, such as crack dimensions.…”

Section: Integrating New Features Beyond Rgb Imagesmentioning

confidence: 99%

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

et al. 2022

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The use of deep learning (DL) in civil inspection, especially in crack detection, has increased over the past years to ensure long-term structural safety and integrity. To achieve a better understanding of the research work on crack detection using DL approaches, this paper aims to provide a bibliometric analysis and review of the current literature on DL-based crack detection published between 2010 and 2022. The search from Web of Science (WoS) and Scopus, two widely accepted bibliographic databases, resulted in 165 articles published in top journals and conferences, showing the rapid increase in publications in this area since 2018. The evolution and state-of-the-art approaches to crack detection using deep learning are reviewed and analyzed based on datasets, network architecture, domain, and performance of each study. Overall, this review article stands as a reference for researchers working in the field of crack detection using deep learning techniques to achieve optimal precision and computational efficiency performance in light of electing the most effective combination of dataset characteristics and network architecture for each domain. Finally, the challenges, gaps, and future directions are provided to researchers to explore various solutions pertaining to (a) automatic recognition of crack type and severity, (b) dataset availability and suitability, (c) efficient data preprocessing techniques, (d) automatic labeling approaches for crack detection, (e) parameter tuning and optimization, (f) using 3D images and data fusion, (g) real-time crack detection, and (h) increasing segmentation accuracy at the pixel level.

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“…In recent years, various semi- or fully automated techniques have been reported in the literature for narrow and enclosed space inspections for building maintenance tasks. Here, computer vision algorithms were used for automatically detecting defects from images collected by inspection tools such as borescope cameras [ 1 , 2 ] and drones [ 3 , 4 ]. However, borescope cameras and drone-based methods have many practical difficulties when used as inspection tools in false ceiling environments.…”

Section: Related Workmentioning

confidence: 99%

False Ceiling Deterioration Detection and Mapping Using a Deep Learning Framework and the Teleoperated Reconfigurable ‘Falcon’ Robot

Semwal

Elara

Melvin

et al. 2021

Sensors

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Periodic inspection of false ceilings is mandatory to ensure building and human safety. Generally, false ceiling inspection includes identifying structural defects, degradation in Heating, Ventilation, and Air Conditioning (HVAC) systems, electrical wire damage, and pest infestation. Human-assisted false ceiling inspection is a laborious and risky task. This work presents a false ceiling deterioration detection and mapping framework using a deep-neural-network-based object detection algorithm and the teleoperated `Falcon’ robot. The object detection algorithm was trained with our custom false ceiling deterioration image dataset composed of four classes: structural defects (spalling, cracks, pitted surfaces, and water damage), degradation in HVAC systems (corrosion, molding, and pipe damage), electrical damage (frayed wires), and infestation (termites and rodents). The efficiency of the trained CNN algorithm and deterioration mapping was evaluated through various experiments and real-time field trials. The experimental results indicate that the deterioration detection and mapping results were accurate in a real false-ceiling environment and achieved an 89.53% detection accuracy.

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Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies

Cited by 28 publications

References 38 publications

Review: Research on product surface quality inspection technology based on 3D point cloud

Review: Research on product surface quality inspection technology based on 3D point cloud

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

False Ceiling Deterioration Detection and Mapping Using a Deep Learning Framework and the Teleoperated Reconfigurable ‘Falcon’ Robot

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