CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection

Huo, Ju; Li, Wei; Tighe, Susan; Xu, Zhengchao; Zhai, Junzhi

doi:10.1002/stc.2551

Cited by 211 publications

(96 citation statements)

References 36 publications

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“…The proposed method achieved better detection precision and can recognize the minimum angle of 10°. Huyan et al 37 proposed a CrackU‐net for pixelwise pavement crack detection. The precision and recall were found to be 98.6% and 97.8%, respectively, outperforming those for FCN and U‐net on their established dataset.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings

Zhao

Shadabfar

Zhang

et al. 2021

Struct Control Health Monit

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This paper presents an approach for the integrated process of classification and instance segmentation of leakage-area and scaling images from shield tunnel linings. For this purpose, the previously established dataset of leakage-area images by the authors is enlarged by means of adding scaling ones. Afterwards, data augmentation is implemented to enrich the database in the classification dataset, and the augmented classification dataset contains 5776 images. The instance segmentation dataset is subsequently enlarged through original images without any data augmentation, including 1496 images. Then a residual net with 101 layers (i.e., ResNet-101) is applied to the classification dataset to obtain a model that can identify leakage-area and scaling images from those of shield tunnel linings. The ResNet-101 classification model achieves an accuracy of 93.37% in terms of testing classification dataset. Moreover, a mask region-based convolutional neural network (Mask R-CNN) is utilized to perform instance segmentation of leakage areas and scaling in the images classified by the ResNet-101 model. The segmentation results of the Mask R-CNN model show 96.1% and 95.6% average precision (AP) with intersection over union (IoU) of 0.5 for bounding box and mask predication, respectively. By using the proposed approach, the leakage-area and scaling defects can be automatically classified and quantified with an overall accuracy of 89.3%, which is quite promising compared to the inherent uncertainty in geotechnical engineering. Image mosaicing is finally applied to provide inspectors with the intuitive observation of the location and distribution information of defects on the tunnel lining.

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Section: Introductionmentioning

confidence: 99%

“…The proposed method achieved better detection precision and can recognize the minimum angle of 10 . Huyan et al 37 proposed a CrackU-net for pixelwise pavement crack detection.…”

mentioning

confidence: 99%

Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings

Zhao

Shadabfar

Zhang

et al. 2021

Struct Control Health Monit

View full text Add to dashboard Cite

show abstract

“…The first is the AdaGrad algorithm, which adopts a specific learning rate for each parameter to improve the performance of the spare gradient. The second is the RMSProp algorithm, which adaptively assigns a learning rate to each parameter based on the average of the closest magnitude of the weighted gradient [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

“…Fin analyze and discuss the experimental results. RMSProp algorithm, which adaptively assigns a learning rate to each parameter base the average of the closest magnitude of the weighted gradient [44].…”

Section: Experiments and Analysismentioning

confidence: 99%

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Qiao

Liu

et al. 2021

Sensors

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Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

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“…What is more, besides studying how to use deep learning algorithms to solve practical problems, some scholars have also proposed to improve the algorithms based on the characteristics of cracks, so that they can detect and segment the cracks better. 17,18 Deep learning and CNN have applications in ancient architecture as well. Llamas et al 19,20 Belhi et al 21 and Obeso et al 22 used CNN to classify heritage buildings.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms

Zou

Zhao

2021

Struct Control Health Monit

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Ancient architectures have a lot of distinctive patterns on their component surfaces. But due to the wind and the Sun, the patterns will present extensive deficiencies, which are not conducive to the routine inspections and the subsequent repair work. To overcome these limits, an automatic deep learning-based method of segmentation, inpainting, and classification for defective patterns on ancient architecture is proposed. First, You Only Look At CoefficienTs, which is a real-time instance segmentation network, is applied to obtain the mask of the defective parts. Then Generative Image Inpainting with Contextual Attention, which is an image inpainting algorithm, is used to reconstruct the defective parts. Finally, Residual Neural Network-50 is used to classify the reconstructed images. In this paper, three types of dragon motifs in the Forbidden City were studied. The results show that the classification accuracy of the reconstructed images is increased by an average of 13.1%, with the maximum increasing by 23.5%. The proposed methods can prepare for future routine inspections in advance.

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CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection

Cited by 211 publications

References 36 publications

Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings

Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms

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