CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework

Yuan, Genji; Li, Jianbo; Meng, Xiangxu; Li, Yinong

doi:10.1049/itr2.12173

Cited by 21 publications

(12 citation statements)

References 75 publications

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“…However, the accuracy of the outcomes was predicted to be 99% where the error rate is at 1%. Contrarily, several authors [1][2][3]29,30] focused and examined exclusively upon the vision based crack detection models. They claimed that though the traditional models are better in accuracy than the vision transformer models that averagely produce outcomes that are of 95% accurate, the transformer models are rapid, robust, incurs lesser costs and lesser time for computing and processing.…”

Section: Traditional Methods Versus Contemporary Methodsmentioning

confidence: 99%

“…In the recent years the researches on the structural health of concrete has increased rapidly. The study of infrastructures on the dams, buildings, bridges and roads have been done by the investigators for the health, impact, mishandlings and other structural observations [1]. The expansion and contraction of the concrete structures in the buildings have been observed as the primary reason to study the health of the concrete structures.…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning for crack detection on concrete structures using CNN architecture

Padmapoorani¹,

Senthilkumar²

2023

Matéria (Rio J.)

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Cracks in concrete structures are caused due to contraction and expansion irregularities, from potential damages caused in the buildings. These irregularities and damages are assessed by the engineers manually or through identification and prediction models with machine learning techniques to evaluate the impact and significance of the structural health in buildings. This research aims at applying machine learning based on VGG16-Net model for the detection of cracks in concrete structures. The proposed model is of CNN (convolutional neural network) + VGG neural network based architecture. The study uses the gradient boosting algorithm for image segmentation. The datasets are obtained from "Kaggle" resource and the library used is 'Hugging Face Transformers'. To evaluate the developed models' performance metrics such as "accuracy, precision, recall and f1-score" are used. The 'accuracy' score obtained is compared against the 'ViT' (Google transformer) accuracy rate, for comparison. The proposed model achieved 98% validation accuracy rate with 0.3% loss. Thus the developed research contributes an innovative and a novel ML model that predicts and identifies the cracks in concrete structures with less loss and higher accuracy with CNN architecture than ViT (vision transformer) models. Current study also provides more input upon CNN being more accurate than ViT models for future researchers for comparative analyses.

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Section: Traditional Methods Versus Contemporary Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of machine learning for crack detection on concrete structures using CNN architecture

Padmapoorani¹,

Senthilkumar²

2023

Matéria (Rio J.)

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“…For instance, Liu et al 22 proposed a two-step CNN with spatial channel squeeze and excitation modules to gradually improve the accuracy of pavement crack segmentation. Yuan et al 23 introduced residual detail attention and fuse features at different scales in a deep CNN to achieve state-of-the-art performance on four crack image datasets. These complex neural network models follow the parameter settings of the classical networks, such as the number of network layers and feature channels.…”

Section: Introductionmentioning

confidence: 99%

A lightweight deep learning network based on knowledge distillation for applications of efficient crack segmentation on embedded devices

Chen

Liu

Hou

2023

Structural Health Monitoring

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Timely crack detection of pavement helps inspectors access road conditions and determine the maintenance strategy, which can reduce repair costs and safety risks. Deep learning has greatly advanced the development of automated crack detection, but there are still challenges that hinder the application of crack segmentation networks in engineering practice such as the bloated models, the class imbalance problem, and the high-performance device dependency. For efficient crack segmentation tasks, this paper proposes a novel high-performance lightweight network termed multi-path convolution feature fusion lightweight network (MCFF-L Net) and utilize the concept of knowledge distillation. The MCFF-L Net with only 1.18 M parameters achieves F1 score of 85.70% and intersection over union (IoU) of 78.22%, which surpasses the popular heavyweight networks and lightweight networks. The proposed network is further implemented on an embedded device of Jetson Xavier NX and the detection speed of pavement cracks can reach 9.71 frames per second (FPS). The combination of embedded device and proposed lightweight networks is suitable for application scenarios where the portable and efficient crack segmentation is needed and the reliable data transmission through network is not accessible.

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“…Detection networks based on deep convolutional neural networks have become the most popular algorithms among researchers in the area of pavement distress detection [1][2][3][4][5][6][7][8][9][10][11][12]. With the development of deep learning theory and the improvement of computer hardware performance, the depth and breadth of detection networks have been increasing to achieve superior accuracy, along with a rapid increase in the number of parameters.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Pavement Distress Detection Using a Morphological Constraints-Based Data Augmentation Method

Dai

Sun

et al. 2023

Coatings

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Pavement distress data in a single section usually presents a long-tailed distribution, with potholes, sealed cracks, and other distresses normally located at the tail. This distribution will seriously affect the performance and robustness of big data-driven deep learning detection models. Conventional data augmentation algorithms only expand the amount of data by image transformation and fail to enlarge the data diversity. Due to such a drawback, this paper proposes a novel two-stage pavement distress image augmentation pattern, in which a mask is generated randomly according to the geometric features of the distress in the first stage; and in the second stage, a distress-free pavement image with the fused mask is transformed into a pavement distress image. Furthermore, two convolutional networks, M-DCGAN and MDTMN, are designed to complete the generation task in two stages separately. In comparison with other generation algorithms, the quality and diversity of the generation results of proposed algorithms are better than other algorithms. In addition, distress detection tests are conducted which indicate that the expanded dataset can raise the IoU from 48.83% to 83.65% at maximum, and the augmented data by the proposed algorithm contributes more to the detection performance.

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CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework

Cited by 21 publications

References 75 publications

Application of machine learning for crack detection on concrete structures using CNN architecture

Application of machine learning for crack detection on concrete structures using CNN architecture

A lightweight deep learning network based on knowledge distillation for applications of efficient crack segmentation on embedded devices

Enhancing Pavement Distress Detection Using a Morphological Constraints-Based Data Augmentation Method

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