Datasets and Methods for Boosting Infrastructure Inspection: A Survey on Defect Classification

Yang, Guidong; Liu, Kangcheng; Zhao, Zuoquan; Zhang, Jihan; Chen, Xi; Chen, Ben M.

doi:10.1109/icca54724.2022.9831922

Cited by 6 publications

(2 citation statements)

References 60 publications

(32 reference statements)

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“…The visual sensors have been emerging as novel techniques for modeling the environment [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], and has been widely adopted for robotic applications. Various visual sensors and Light Detection And Ranging Sensors (LiDAR) sensors have been widely deployed in various robotics platforms such as automatically navigated aerial robots and ground robots [16], [17], [18], [19], [20], [21]. For example, the SLAM is widely adopted for robot localization, mapping, and navigation applications [11], [22], [23], [12], [24], [25], [26], [27], [28], [29], [30], [31], [22].…”

Section: B the Overall System For Uav Inspectionsmentioning

confidence: 99%

Industrial UAV-Based Unsupervised Domain Adaptive Crack Recognitions: From Database Towards Real-Site Infrastructural Inspections

Liu

Chen

2023

IEEE Trans. Ind. Electron.

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Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and its performance of it is compared with other state-of-the-art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmark dataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed and will be established for the crack inspection tasks of various concrete structures. All our self-established dataset and codes are opensourced at: https://github.com/KangchengLiu/Crack-Detectionand-Segmentation-Dataset-for-UAV-Inspection.

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Section: B the Overall System For Uav Inspectionsmentioning

confidence: 99%

Industrial UAV-Based Unsupervised Domain Adaptive Crack Recognitions: From Database Towards Real-Site Infrastructural Inspections

Liu

Chen

2023

IEEE Trans. Ind. Electron.

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“…Civil infrastructure including bridges, roads and tunnels remain vulnerable to deterioration due to the occurrence of disasters, along with cyclical loading and harsh environmental factors [1]. Timely detection and simultaneous maintenance of civil infrastructure is an indispensable way to ensure human safety and reduction in the costs associated with infrastructural damages [2,3]. As reported in the literature, structural deficiency along with aging and respective failure, have been associated with the damages which ultimately lead to the collapse of the majority of bridges (~ 46%) [4].…”

Section: Introductionmentioning

confidence: 99%

Modern Crack Detection for Bridge Infrastructure Maintenance Using Machine Learning

Munawar

Hammad

Waller

et al. 2022

Hum-Cent Intell Syst

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Manual investigation of damages incurred to infrastructure is a challenging process, in that it is not only labour-intensive and expensive but also inefficient and error-prone. To automate the process, a method that is based on computer vision for automatically detecting cracks from 2D images is a viable option. Amongst the different methods of deep learning that are commonly used, the convolutional neural network (CNNs) is one that provides the opportunity for end-to-end mapping/learning of image features instead of using the manual suboptimal image feature extraction. Specifically, CNNs do not require human supervision and are more suitable to be used for indoor and outdoor applications requiring image feature extraction and are less influenced by internal and external noise. Additionally, the CNN’s are also computationally efficient since they are based on special convolution layers and pooling operations that enable the full execution of CNN frameworks on several hardware devices. Keeping this in mind, we propose a deep CNN framework that is based on 10 different convolution layers along with a cycle GAN (Generative Adversarial Network) for predicting the crack segmentation pixel by pixel in an end-to-end manner. The methods proposed here include the Deeply Supervised Nets (DSN) and Fully Convolutional Networks (FCN). The use of DSN enables integrated feature supervision for each stage of convolution. Furthermore, the model has been designed intricately for learning and aggregating multi-level and multiscale features while moving from the lower to higher convolutional layers through training. Hence, the architecture in use here is unique from the ones in practice which just use the final convolution layer. In addition, to further refine the predicted results, we have used a guided filter and CRFs (Conditional Random Fields) based methods. The verification step for the proposed framework was carried out with a set of 537 images. The deep hierarchical CNN framework of 10 convolutional layers and the Guided filtering achieved high-tech and advanced performance on the acquired dataset, showing higher F-score, Recall and Precision values of 0.870, 0.861, and 0.881 respectively, as compared to the traditional methods such as SegNet, Crack-BN, and Crack-GF.

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RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging

Liu

2022

Int J Comput Vis

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Existing state-of-the-art 3D point cloud understanding methods merely perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework that simultaneously solves the downstream high-level understanding tasks including both segmentation and detection, especially when labels are extremely limited. This work presents a general and simple framework to tackle point cloud understanding when labels are limited. The first contribution is that we have done extensive methodology comparisons of traditional and learned 3D descriptors for the task of weakly supervised 3D scene understanding, and validated that our adapted traditional PFH-based 3D descriptors show excellent generalization ability across different domains. The second contribution is that we proposed a learning-based region merging strategy based on the affinity provided by both the traditional/learned 3D descriptors and learned semantics. The merging process takes both low-level geometric and high-level semantic feature correlations into consideration. Experimental results demonstrate that our framework has the best performance among the three most important weakly supervised point clouds understanding tasks including semantic segmentation, instance segmentation, and object detection even when very limited number of points are labeled. Our method, termed Region Merging 3D (RM3D), has superior performance on Scan-Net data-efficient learning online benchmarks and other four large-scale 3D understanding benchmarks under various experimental settings, outperforming current arts by a margin for various 3D understanding tasks without complicated learning strategies such as active learning.

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Datasets and Methods for Boosting Infrastructure Inspection: A Survey on Defect Classification

Cited by 6 publications

References 60 publications

Industrial UAV-Based Unsupervised Domain Adaptive Crack Recognitions: From Database Towards Real-Site Infrastructural Inspections

Industrial UAV-Based Unsupervised Domain Adaptive Crack Recognitions: From Database Towards Real-Site Infrastructural Inspections

Modern Crack Detection for Bridge Infrastructure Maintenance Using Machine Learning

RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging

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