Comparative Analysis of Deep Neural Networks for Crack Image Classification

Mohamed, Sheerin Sitara Noor; Srinivasan, Kavitha

doi:10.1007/978-3-030-34080-3_49

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Also, the dataset used in [26] is considered for validating the proposed HIE algorithm using machine learning techniques. Further, [27] suggested that the enhanced crack images improved their performance by 1% to 12% with the CNN and pre-trained VGG16, VGG19 and Inception ResNet models. Images without enhancement, on the contrary, showed little improvement, thus justifying the importance of enhancement algorithms for crack images.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Image Enhancement Algorithm for Effective Concrete Surface Crack Classification

Mohamed¹,

Srinivasan²

2021

JUSST

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Huge number of images are acquired and analysed every day for a range of applications in civil infrastructure. One such application is the identification of cracks in concrete surface images, which is a challenge owing to their low contrast and resolution, blurriness, noise and information loss. Existing image enhancement algorithms improve either contrast or resolution to a rather limited extent. This paper proposes a Hybrid Image Enhancement (HIE) algorithm to improve both the contrast and resolution of concrete surface images using the Wavelet transform and Singular Value Decomposition (SVM). The enhanced concrete surface crack images are classified into specific crack types. The classification comprises preprocessing, crack detection, feature extraction and crack classification. The images are initially preprocessed using the Wiener filter to remove blurriness, following which cracks are detected using morphological operations and discontinuities in the segmented crack regions eliminated using the K-Dimensional Tree algorithm. Features are extracted from the segmented regions using statistical and geometric features. The image is classified thereafter into specific crack types using algorithms from three different neural network, kernel and tree based categories. The proposed HIE algorithm is validated using quantitative metrics and the results obtained are compared with those from State-of-the-Art methods and datasets. The results have shown that the HIE algorithm offers significantly improved accuracy of between 6% and 10% in the classification of concrete surface images.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Image Enhancement Algorithm for Effective Concrete Surface Crack Classification

Mohamed¹,

Srinivasan²

2021

JUSST

Self Cite

View full text Add to dashboard Cite

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Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach

Kanthavel¹

2021

JAICN

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Recently, glass crack detection methods have been emerging in Artificial intelligence programming. The early detection of the crack in glass could save many lives. Glass fractures can be detected automatically using machine vision. However, this has not been extensively researched. As a result, a detection algorithm is a benefit to study the mechanics of glass cracking. To test the algorithm, benchmark data are used and analysed. According to the first findings, the algorithm is capable of figuring out the screen more or less correctly and identifying the main fracture structures with sufficient efficiency required for majority of the applications. This research article has addressed the early detection of glass cracks by using edge detection, which delivers excellent accuracy in fracture identification. Following the pre-processing stage, the CNN technique extracts additional characteristics from the input pictures that have been provided due to dense feature extraction. The "Adam" optimizer is used to update the bias weights of networks in a cost-effective manner. Early identification is achievable with high accuracy metrics when using these approaches, as shown in the findings and discussion part of this paper.

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Comparative Analysis of Deep Neural Networks for Crack Image Classification

Cited by 2 publications

References 11 publications

A Hybrid Image Enhancement Algorithm for Effective Concrete Surface Crack Classification

A Hybrid Image Enhancement Algorithm for Effective Concrete Surface Crack Classification

Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach

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