Quantitative road crack evaluation by a U‐Net architecture using smartphone images and Lidar data

Yamaguchi, Takahiro; Mizutani, Tsukasa

doi:10.1111/mice.13071

Cited by 8 publications

(2 citation statements)

References 72 publications

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“…Wu et al (2019) pruned and utilized VGG16 to classify corrosion and crack defects in bridge structures, achieving accuracy rates of 93.6% and 98.5%, respectively. Novel networks like Faster Region-CNN (Faster RCNN) and You Only Look Once (YOLO) series (Chun et al, 2023; have emerged for object detection, while Mask RCNN and U-Net (Yamaguchi & Mizutani, 2023) are employed for seg-mentation. Recent researchers proposed EfficientNet and incorporated attention mechanisms (Chen & He, 2022;Y.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

Zhang,

Shen,

Lin

et al. 2023

Computer aided Civil Eng

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Bridge inspection ensures that in‐service bridges are managed and maintained in conformity. To enhance the accuracy and efficiency of bridge inspection, an automatic hierarchical model is proposed, which enables the classification and correlation of bridge surface images at three levels, namely, at the structure, component, and defect type level. Thus, the impact of both the defect types and the affected components on bridge safety can be simultaneously considered. The proposed model uses a group of sub‐models instead of the common flat network to realize the multiple tasks, which is advantageous in accuracy, training simplicity, and scalability. The classification accuracy of the hierarchical model in three levels has reached 96%, 92%, and 81%. Results demonstrate the effectiveness of the proposed method in the classification of multi‐scale targets. This study may provide a new strategy for developing a systematic and easily adaptable detection framework for practical bridge engineering.

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Section: Introductionmentioning

confidence: 99%

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

Zhang,

Shen,

Lin

et al. 2023

Computer aided Civil Eng

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“…These automated systems can be employed to inspect the dimensional accuracy of factory-produced construction components, such as precast concrete (PC) elements, thereby preventing quality issues that may arise during or after the construction process (Kim et al, 2016;Son & Han, 2023). With the use of construction robots or drones, defects such as incorrect dimensions or cracks in construction components can be measured, thus enabling more efficient construction tasks (Shi et al, 2023;Yamaguchi & Mizutani, 2022). Additionally, even after the completion of construction, these technologies can be utilized to monitor the displacement of various structural elements, evaluate the safety and stability of the structure, and facilitate ongoing maintenance of the building (Yin et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Park

2023

Computer aided Civil Eng

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This study presents a novel, deep‐learning‐based model for the automated reconstruction of a cross‐sectional drawing from stereo photographs. Targeted cross‐sections captured in stereo photographs are detected and translated into sectional drawings using faster region‐based convolutional neural networks and Pix2Pix generative adversarial network. To address the challenge of perspective correction in the photographs, a novel camera pose optimization method is introduced and employed. This method eliminates the need for camera calibration and image matching, thereby offering greater flexibility in camera positioning and facilitating the use of telephoto lenses while avoiding image‐matching errors. Moreover, synthetic image datasets are used for training to facilitate the practical implementation of the proposed model in construction industry applications, considering the limited availability of open datasets in this field. The applicability of the proposed model was evaluated through experiments conducted on the cross‐sections of curtain wall components. The results demonstrated superior measurement accuracy, compared with those of current methods of laser scanning or camera‐based measurements for construction components.

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Deep learning-based intelligent detection of pavement distress

Zheng,

Xiao,

Wang

et al. 2024

Automation in Construction

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Quantitative road crack evaluation by a U‐Net architecture using smartphone images and Lidar data

Cited by 8 publications

References 72 publications

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Deep learning-based intelligent detection of pavement distress

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