Automatic crack inspection for concrete bridge bottom surfaces based on machine vision

Zhang, Hui; Tan, Jinwen; Liu, Li; Wu, Q. M. Jonathan; Wang, Yaonan; Liu, Jie

doi:10.1109/cac.2017.8243654

Cited by 14 publications

(9 citation statements)

References 19 publications

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“…Many crack detection algorithms based on deep learning have been recently proposed. Zhang et al [23] designed a framework that can be directly applied to the original crack image for automatic feature extraction and classification, and the framework achieved superior performance compared to traditional handcrafted methods. Pauly et al [24] improved classification accuracy and recognition by adopting a deeper neural network to classify crack and non-crack patches.…”

Section: Related Workmentioning

confidence: 99%

MSCNet: A Framework With a Texture Enhancement Mechanism and Feature Aggregation for Crack Detection

Wang

et al. 2022

IEEE Access

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Bridge crack is one of the critical optical and visual information to judge the health state of bridges. The bridge crack detection methods based on artificial intelligence are essential in this field, but the current approaches are not satisfactory in terms of speed and accuracy. This study proposes a novel multi-scale crack detection network, called MSCNet, comprising a texture enhancement mechanism and feature aggregation to enhance the visual saliency of the objects in the background for bridge crack detection. We use Res2Net as the backbone network to improve the depth information expression ability of the cracks itself. Because the edge property of bridge cracks is prominent, to make full use of this visual feature, we use a texture enhancement module based on group attention to capturing the detailed information of cracks in low-level features. To further mine the depth information of the network, we use a cascade fusion module to capture crack location information in high-level features. Finally, to fully utilize the characteristic information of the deep network, we fuse the low-and high-level features to obtain the final crack prediction. We evaluate the proposed method compared with other state-of-the-art methods on a large-scale crack dataset. The experimental results demonstrate the effectiveness and superiority of the proposed method, which achieves a precision of 93.5%, recall of 94.2%, and inference speed of over 63 FPS.

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Section: Related Workmentioning

confidence: 99%

MSCNet: A Framework With a Texture Enhancement Mechanism and Feature Aggregation for Crack Detection

Wang

et al. 2022

IEEE Access

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“…In addition, the traditional region-proposal methods are inefficient in selecting good candidate regions from the noisy images (Uijlings et al , 2013). To improve the computation efficacy of region-based methods, Zhang et al (2017a) applied parallel processing; however, the computation and resource costs were expensive.…”

Section: Crack Detection-based Deep Learning: a Conceptual Backgroundmentioning

confidence: 99%

Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

Elghaish

Matarneh

Talebi

et al. 2021

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Purpose The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection. Design/methodology/approach A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations. Findings The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results. Practical implications The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. Originality/value A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

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“…It is required to capture the speckle pattern before and after the deformation and the results are sensitive to the relative locations of the cameras and objects, which is not applicable to the structural inspection. Some researchers have developed robotic or vehicular visual inspection systems to acquire surface pictures of bridges and tunnels, improving the efficiency of the process in terms of time consumption, cost, and safety (Huang et al., 2017; Jiang et al., 2019; Prasanna et al., 2016; H. Zhang et al., 2017). The detailed features of cracks, such as width, length, and orientation, are critical for the cracking analysis and health evaluation of infrastructures (Gribniak et al., 2016; Jiang et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Conventional image‐segmentation algorithms for crack identification are based on a gray threshold (Dinh et al., 2016; Fujita & Hamamoto, 2011; Talab et al., 2016), edge detection (Abdel‐Qader et al., 2003; Canny, 1986; H. Zhang et al., 2017), and mathematical morphology (Lee et al., 2013; Yun et al., 2015). However, the performance of these methods depends on the preprocessing of the images and is sensitive to crack‐like artifacts, such as dust, shadows, and marks.…”

Section: Introductionmentioning

confidence: 99%

Sparse‐sensing and superpixel‐based segmentation model for concrete cracks

Xie

Cai

Wang

et al. 2022

Computer aided Civil Eng

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Efficient image-recognition algorithms to classify the pixels accurately are required for the computer-vision-based inspection of concrete defects. This study proposes a deep learning-based model called sparse-sensing and superpixelbased segmentation (SSSeg) for accurate and efficient crack segmentation. The model employed a sparse-sensing-based encoder and a superpixel-based decoder and was compared with six state-of-the-art models. An input pipeline of 1231 diverse crack images was specially designed to train and evaluate the models. The results indicated that the SSSeg achieved a good balance between the recognition correctness and completeness and outperformed other models in both accuracy and efficiency. The SSSeg also exhibited good resistance to the interference of surface roughness, dirty stains, and moisture. The increased depth and receptive field of sparse-sensing units guaranteed the representability; meanwhile, structured sparse characteristics protected the network from overfitting. The lightweight superpixel-based decoder omitted skip connections, which greatly reduced the computation and memory footprint and enlarged the input size in the inference.

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Automatic crack inspection for concrete bridge bottom surfaces based on machine vision

Cited by 14 publications

References 19 publications

MSCNet: A Framework With a Texture Enhancement Mechanism and Feature Aggregation for Crack Detection

MSCNet: A Framework With a Texture Enhancement Mechanism and Feature Aggregation for Crack Detection

Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

Sparse‐sensing and superpixel‐based segmentation model for concrete cracks

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