Hybrid Concrete Crack Segmentation and Quantification Across Complex Backgrounds Without a Large Training Dataset

Kang, Dong Ho; Benipal, Sukhpreet S.; Cha, Young‐Jin

doi:10.1007/978-3-030-76004-5_14

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…17 Kang et al achieved a balance between speed and accuracy to a certain extent by integrating faster-RCNN with tubularity flow field (TuFF) for image segmentation. 41 Wang et al obtained high-level features through the encoder and then the decoder gradually recovered the feature maps of the predicted results, resulting in time-consuming segmentation by CNNs and other types of networks. 42 Raza Ali et al conducted research on the application of CNN and other deep learning networks to crack detection and looked forward to the prospect of crack detection using computer vision, but did not mention the detection speed.…”

Section: Deep Learningmentioning

confidence: 99%

“…Li et al proposed to input interleaved low‐rank group convolution hybrid deep network (LIHGCDN)‐type images into SegNet‐DCRF (dense condition random field) for crack segmentation, but the reasoning time was reduced at the cost of increasing the complexity of the model 17 . Kang et al achieved a balance between speed and accuracy to a certain extent by integrating faster‐RCNN with tubularity flow field (TuFF) for image segmentation 41 . Wang et al obtained high‐level features through the encoder and then the decoder gradually recovered the feature maps of the predicted results, resulting in time‐consuming segmentation by CNNs and other types of networks 42 .…”

Section: Related Workmentioning

confidence: 99%

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Automatic bridge crack detection using boundary refinement based on real‐time segmentation network

Liu

Fang

et al. 2022

Structural Contr & Hlth

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Summary The appearance of surface cracks is an important feature of the early safety of the architecture being threatened, which is of great significance to maintaining the safety of the bridge. Bridge safety maintenance is still a challenging issue owing to noise interference and unclear bridge images. The detection method based on deep learning has achieved remarkable result in semantic segmentation, target detection, and other fields, but still be unable to focus on balancing speed and accuracy. Wherefore, we designed a new crack detection network‐dense boundary refinement network (DBR‐Net), which combines the advantages of STDC‐Net (Short‐Term Dense Concatenate network) and refinement network. STDC‐Net mainly improves the detection rate by eliminating redundant structures and optimizes the detailed information by binary cross‐entropy loss and dice loss. Finally, the detail aggregation module combines the shallow spatial details with the deep semantic information to predict the segmentation results. Among them, the refinement network refines the segmentation results of the offset map generated by the training stage and the test stage. We verified the feasibility of DBR‐Net on three datasets. At the same time, the detection accuracy rate on our self‐made crack dataset reached 97.54%. What's more worth mentioning is that our detection rate has reached 37.0 images per second (IPS), realizing real‐time crack detection.

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Section: Deep Learningmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic bridge crack detection using boundary refinement based on real‐time segmentation network

Liu

Fang

et al. 2022

Structural Contr & Hlth

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“…Thus the approach performs well on multiple sample sets. Kang et al [18], [47], [48] perform crack segmentation in complex environments and different lighting conditions by integrating three independent computer vision algorithms and developed a new encoder with an attention module. Choi et al [49] propose a real-time crack segmentation DL architecture, referred to as SDDNet-V1, which can greatly improve the time efficiency and identify relatively vague cracks.…”

Section: B Deep Learningmentioning

confidence: 99%

“…In recent years, with the advancement of remote sensing technology and computer algorithms, high-resolution satellite imagery and deep learning approaches have been used for pavement conditions mapping [2], [17], [18], [19]. However, most of the existing deep learning algorithms focus on pavement damage monitoring and are suitable for fine-scale monitoring of pavement quality in a small range, but not suitable for monitoring large-scale pavement aging processes.…”

mentioning

confidence: 99%

A New Method for Automated Monitoring of Road Pavement Aging Conditions Based on Recurrent Neural Network

Chen

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The automated monitoring of road pavement conditions is a challenging subject in intelligent transportation. However, the existing studies mostly focus on extracting pavement damages such as cracks, while the pavement aging conditions are still less investigated. In this paper, a novel method based on a modified recurrent neural network is designed for automated monitoring of asphalt pavement aging phenomena from fineresolution satellite imagery. A spectral augmentation method is proposed to enhance the spectral details of the road pavements. A novel loss function is also proposed to improve the bi-directional gated recurrent unit (Bi-GRU) network in order to better classify different degrees of road pavement aging and nonpavement objects. In order to demonstrate the outperformance of the modified network Bi-GRU+, the Worldview-2 satellite image (16360 * 7728) covering 16 asphalt roads in the southwestern suburb of Beijing City is used. The results show that the proposed approach has better performance than existing machine learning methods, with an overall accuracy of 98.16% and a Kappa coefficient of 0.97. The overall processing time of the proposed method is 7836 seconds in our case study. The proposed method is efficient for large-scale monitoring of road health conditions from fine-resolution satellite imagery. It can become a part of intelligent transportation and provide a new foundation for largerange automated monitoring of road pavement aging conditions.

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“…In this paper, we propose a novel DL-based architecture, PSNet based on the corresponding authors' extensive experiences and knowledge with SOTA performances in deep learning networks, as shown in 33 and 34 . As well as in detection and segmentation problems as shown in [35][36][37] , and 38 . To improve upon the boundary pixel limitations of previous polyp segmentation networks and to improve upon overall polyp segmentation capabilities, we have designed PSNet, a unique dual-model architecture, an encoder-decoder-based end-to-end network.…”

mentioning

confidence: 99%

Dual encoder–decoder-based deep polyp segmentation network for colonoscopy images

Lewis

Cha

Kim

2023

Sci Rep

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Detection of colorectal polyps through colonoscopy is an essential practice in prevention of colorectal cancers. However, the method itself is labor intensive and is subject to human error. With the advent of deep learning-based methodologies, and specifically convolutional neural networks, an opportunity to improve upon the prognosis of potential patients suffering with colorectal cancer has appeared with automated detection and segmentation of polyps. Polyp segmentation is subject to a number of problems such as model overfitting and generalization, poor definition of boundary pixels, as well as the model’s ability to capture the practical range in textures, sizes, and colors. In an effort to address these challenges, we propose a dual encoder–decoder solution named Polyp Segmentation Network (PSNet). Both the dual encoder and decoder were developed by the comprehensive combination of a variety of deep learning modules, including the PS encoder, transformer encoder, PS decoder, enhanced dilated transformer decoder, partial decoder, and merge module. PSNet outperforms state-of-the-art results through an extensive comparative study against 5 existing polyp datasets with respect to both mDice and mIoU at 0.863 and 0.797, respectively. With our new modified polyp dataset we obtain an mDice and mIoU of 0.941 and 0.897 respectively.

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Hybrid Concrete Crack Segmentation and Quantification Across Complex Backgrounds Without a Large Training Dataset

Cited by 4 publications

References 14 publications

Automatic bridge crack detection using boundary refinement based on real‐time segmentation network

Automatic bridge crack detection using boundary refinement based on real‐time segmentation network

A New Method for Automated Monitoring of Road Pavement Aging Conditions Based on Recurrent Neural Network

Dual encoder–decoder-based deep polyp segmentation network for colonoscopy images

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