Anomaly detection of defects on concrete structures with the convolutional autoencoder

Chow, Jun Kang; Su, Zhibin; Wu, Jiaqi; Tan, Pin Siang; Mao, Xin; Wang, Yu-Hsing

doi:10.1016/j.aei.2020.101105

Cited by 227 publications

(76 citation statements)

References 37 publications

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“…After the first general pictures are taken, they are analyzed to check the condition of the viaduct, paying special attention to those areas where a defect is suspected. This analysis can be performed manually by an inspector or automatically by applying an automatic image defect detection algorithm, like [28,29]. After the analysis, a decision should be made to determine if more detailed information on the defects found or suspect areas are required.…”

Section: System Descriptionmentioning

confidence: 99%

Aerial Robotic Solution for Detailed Inspection of Viaducts

et al. 2021

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The inspection of public infrastructure, such as viaducts and bridges, is crucial for their proper maintenance given the heavy use of many of them. Current inspection techniques are very costly and manual, requiring highly qualified personnel and involving many risks. This article presents a novel solution for the detailed inspection of viaducts using aerial robotic platforms. The system provides a highly automated visual inspection platform that does not rely on GPS and could even fly underneath the infrastructure. Unlike commercially available solutions, our system automatically references the inspection to a global coordinate system usable throughout the lifespan of the infrastructure. In addition, the system includes another aerial platform with a robotic arm to make contact inspections of detected defects, thus providing information that cannot be obtained only with images. Both aerial robotic platforms feature flexibility in the choice of camera or contact measurement sensors as the situation requires. The system was validated by performing inspection flights on real viaducts.

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Section: System Descriptionmentioning

confidence: 99%

Aerial Robotic Solution for Detailed Inspection of Viaducts

et al. 2021

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“…There have been many studies examining how region-based CNNs can augment current work in concrete crack detection [ 47 , 48 , 49 ]. Autoencoders have also become increasingly common for crack detection tasks [ 50 , 51 , 52 ].…”

Section: Introductionmentioning

confidence: 99%

Crack Detection in Images of Masonry Using CNNs

Hallee

Napolitano

Reinhart

et al. 2021

Sensors

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While there is a significant body of research on crack detection by computer vision methods in concrete and asphalt, less attention has been given to masonry. We train a convolutional neural network (CNN) on images of brick walls built in a laboratory environment and test its ability to detect cracks in images of brick-and-mortar structures both in the laboratory and on real-world images taken from the internet. We also compare the performance of the CNN to a variety of simpler classifiers operating on handcrafted features. We find that the CNN performed better on the domain adaptation from laboratory to real-world images than these simple models. However, we also find that performance is significantly better in performing the reverse domain adaptation task, where the simple classifiers are trained on real-world images and tested on the laboratory images. This work demonstrates the ability to detect cracks in images of masonry using a variety of machine learning methods and provides guidance for improving the reliability of such models when performing domain adaptation for crack detection in masonry.

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“…For example, J. Yu et al [17] proposed a two-dimensional principal component analysis-based convolutional autoencoder network for detecting defects on wafer maps. J. K. Chow et al [18] utilized a convolutional autoencoder network for detecting the defect on concrete structure. Compared with the other segmentation models, the convolutional autoencoder network was adaptable for detecting the defect with a wide range of scale.…”

Section: Introductionmentioning

confidence: 99%

Developing the Smart Sorting Screw System Based on Deep Learning Approaches

et al. 2021

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The deep learning technique has turned into a mature technique. In addition, many researchers have applied deep learning methods to classify products into defective categories. However, due to the limitations of the devices, the images from factories cannot be trained and inferenced in real-time. As a result, the AI technology could not be widely implemented in actual factory inspections. In this study, the proposed smart sorting screw system combines the internet of things technique and an anomaly network for detecting the defective region of the screw product. The proposed system has three prominent characteristics. First, the spiral screw images are stitched into a panoramic image to comprehensively detect the defective region that appears on the screw surface. Second, the anomaly network comprising of convolutional autoencoder (CAE) and adversarial autoencoder (AAE) networks is utilized to automatically recognize the defective areas in the absence of a defective-free image for model training. Third, the IoT technique is employed to upload the screw image to the cloud platform for model training and inference, in order to determine if the defective screw product is a pass or fail on the production line. The experimental results show that the image stitching method can precisely merge the spiral screw image to the panoramic image. Among these two anomaly models, the AAE network obtained the best maximum IOU of 0.41 and a maximum dice coefficient score of 0.59. The proposed system has the ability to automatically detect a defective screw image, which is helpful in reducing the flow of the defective products in order to enhance product quality.

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Anomaly detection of defects on concrete structures with the convolutional autoencoder

Cited by 227 publications

References 37 publications

Aerial Robotic Solution for Detailed Inspection of Viaducts

Aerial Robotic Solution for Detailed Inspection of Viaducts

Crack Detection in Images of Masonry Using CNNs

Developing the Smart Sorting Screw System Based on Deep Learning Approaches

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