Convolution neural network model for an intelligent solution for crack detection in pavement images

Rababaah, Aaron Rasheed; Wolfer, James

doi:10.1504/ijcat.2022.10050313

Cited by 4 publications

(3 citation statements)

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“…These innovative approaches allowed systems to dynamically learn from new data, adjusting and improving autonomously, thereby enhancing the accuracy and efficiency of crack identification processes. This evolution was particularly pivotal for on-site applications, where instant analysis and decisions are crucial [26].…”

Section: F Adaptive Learning and Real-time Processingmentioning

confidence: 99%

Deep CNN Approach with Visual Features for Real-Time Pavement Crack Detection

Kulambayev,

Astaubayeva,

Tleuberdiyeva

et al. 2024

IJACSA

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This research delves into an innovative approach to an age-old urban maintenance challenge: the timely and accurate detection of pavement cracks, a key issue linked to public safety and fiscal efficiency. Harnessing the power of Deep Convolutional Neural Networks (DCNNs), the study introduces a cutting-edge model, meticulously optimized for the nuanced task of identifying fissures in diverse pavement types, under various lighting and environmental conditions. Traditional methodologies often stumble in this regard, plagued by issues of low accuracy and high false-positive rates, predominantly due to their inability to adeptly handle the intricate variations in images caused by shadows, traffic, or debris. This paper propounds a robust algorithm that trains the model using a rich library of images, capturing an array of crack types, from hairline fractures to gaping crevices, thus imbuing the system with an astute 'understanding' of target anomalies. One salient breakthrough detailed is the model's capacity for 'context-aware' analysis, allowing for a more adaptive, precision-driven scrutiny that significantly mitigates the issue of over-generalization common in less sophisticated systems. Furthermore, the research breaks ground by integrating a novel feedback mechanism, enabling the DCNN to learn dynamically from misclassifications in an iterative refinement process, markedly enhancing detection reliability over time. The findings underscore not only improved accuracy but also heightened processing speeds, promising substantial implications for scalable real-world application and establishing a significant leap forward in predictive urban infrastructure maintenance.

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Section: F Adaptive Learning and Real-time Processingmentioning

confidence: 99%

Deep CNN Approach with Visual Features for Real-Time Pavement Crack Detection

Kulambayev,

Astaubayeva,

Tleuberdiyeva

et al. 2024

IJACSA

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“…The activation function itself needs to be nonlinear in order to provide meaning to the multilayer connectivity of the network [28][29]. Without the activation function, the multilayer network connection can be morphed into a one-layer network by transformation, and the network's depth becomes irrelevant.…”

Section: Music Style Recognitionmentioning

confidence: 99%

The Integration of Instrumental Elements and Vocal Music Teaching in Colleges and Universities Based on the Core Literacy View of Music

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, Fourier transforms in big data technology is used to realize the data preprocessing process of vocal signals from analog signals to digital signals and to explore the identification law of various music styles in vocal teaching in colleges and universities. According to the application of a convolutional neural network in vocal music teaching, the evaluation model of vocal music teaching based on a convolutional neural network is constructed by utilizing a convolutional neural network with accurate sensitivity and high-speed eigenvalue training speed. Starting from the design of relevant variables and the selection of evaluation tools, the experimental study of emotional therapy for the integration of percussion instruments and vocal music teaching is designed, and statistical analysis and simulation analysis are used to analyze the integration of musical instruments and vocal music teaching in the context of music core literacy. The results show that the evaluation error of the recurrent neural network method is 10.07%, while the minimum value is 0.24%. The maximum evaluation error of the convolutional neural network method is 8.22%, and the minimum evaluation error is 0.22%. The evaluation results of the quality of the integrated teaching of percussion instruments and vocal music of the model in this paper are better than the evaluation method of recurrent neural networks. During the maintenance period, when subject three was given weekly emotional therapy for teaching percussion instruments and vocal fusion, it was found that the subject’s emotional behavioral problems also completely subsided. This study realizes that the elimination of negative emotions in the teaching of musical instrument elements and college vocal music has a facilitating effect on the formation of students’ core musical literacy perspective.

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“…With the development of machine learning, convolutional neural networks [6] (CNNs) based on deep learning methods have shown promising performance in the field of image segmentation, since these methods can autonomously learn features from image datasets, avoiding the limitations of manual construction of crack features. Currently, CNNs have been applied in the field of remote sensing based crack detection, especially for remote sensing images based on visible light.…”

Section: Introductionmentioning

confidence: 99%

MRHCNet: Multi Resolution Hybrid Convolution based U-Net for Crack Detection with Mix Dilated Linear Pooling and Laplacian Pyramid Mix Module

Zhang,

Huang

2023

Preprint

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Deep learning based methods have achieved the state-of-art results in crack detection. However, these models have some drawbacks. Firstly, these models often use down-sampling operation to reduce the resolution of features, which would cause feature information loss. Secondly, square filtering windows are used to extract strip crack features, which would merge unrelated feature information. Thirdly, these models are limited to learn detailed feature information and edge features of cracks, which would cause detail and edge feature information loss. To solve these problems, a Multi Resolution Hybrid Convolutional Module (MRHCM) is designed to avoid feature information loss caused by down-sampling. Additionally, a Mix Dilated Linear Pooling (MDLP) is proposed to capture the strip feature information of cracks. Furthermore, a Laplacian Pyramid Mix Module (LPMM) is designed to learn detailed feature information of cracks. Also, a Sobel Mix Cross Pooling (SMCP) is used to enhance the network?s ability of learning edge features. Finally, a Multi Resolution Hybrid Convolution based U-Net (MRHCNet) with MDLP, LPMM and SMCP is proposed by us. Experimental results show that our MRHCNet could achieve the accuracy of 0.931, 0.825 and 0.989 on the Cracktree200, CRACK500 and CFD datasets respectively, which is higher than that of the traditional models.

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Convolution neural network model for an intelligent solution for crack detection in pavement images

Cited by 4 publications

References 0 publications

Deep CNN Approach with Visual Features for Real-Time Pavement Crack Detection

Deep CNN Approach with Visual Features for Real-Time Pavement Crack Detection

The Integration of Instrumental Elements and Vocal Music Teaching in Colleges and Universities Based on the Core Literacy View of Music

MRHCNet: Multi Resolution Hybrid Convolution based U-Net for Crack Detection with Mix Dilated Linear Pooling and Laplacian Pyramid Mix Module

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