Road Crack Detection using Support Vector Machine (SVM) and OTSU Algorithm

Sari, Yuslena; Prakoso, Puguh Budi; Baskara, Andreyan Rezky

doi:10.1109/icevt48285.2019.8993969

Cited by 45 publications

(35 citation statements)

References 15 publications

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“…To detect road surface cracks, features of a road cracking are required. The features are shape feature and texture feature, where these features can be used to distinguish road conditions [19,20]. The collected data is labelled as a cracked road image.…”

Section: Methodsmentioning

confidence: 99%

Application of neural network method for road crack detection

2020

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The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in implementing Supervised Learning in computer vision, the applied method for classification is artificial neural network (ANN). Datasets, which are used for evaluation are collected from low-cost smart phones. The results show that feature extraction using GLCM can provide good accuracy that is equal to 90%.

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Section: Methodsmentioning

confidence: 99%

Application of neural network method for road crack detection

2020

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“…A continuous bridge mosaic is created using series of images from the robot. This mosaic is further used to develop crack density maps [57][58][59].…”

Section: Svm and Random Forestmentioning

confidence: 99%

Image-Based Crack Detection Methods: A Review

et al. 2021

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Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

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“…They segmented cracks from the background by applying an iterative threshold algorithm. Sari et al (203) classified and segmented asphalt pavements by deploying the SVM and Otsu methods, respectively. Some of the popular crack detection algorithms are reviewed in (204).…”

Section: Computer Visionmentioning

confidence: 99%

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

Gharehbaghi

Farsangi

Noori

et al. 2021

Arch Computat Methods Eng

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The benefits of tracking, identifying, measuring features of interest from structure responses have endless applications for saving cost, time and improving safety. To date, structural health monitoring (SHM) has been extensively applied in several fields, such as aerospace, automotive, and mechanical engineering. However, the focus of this paper is to provide a comprehensive upto-date review of civil engineering structures such as buildings, bridges, and other infrastructures. For this reason, this article commences with a concise introduction to the fundamental definitions of SHM. The next section presents the general concepts and factors that determine the best strategy to be employed for SHM. Afterward, a thorough review of the most prevalent anomaly detection approaches, from classic techniques to advanced methods, is presented. Subsequently, some popular benchmarks, including laboratory specimens and real structures for validating the proposed methodologies, are demonstrated and discussed. Finally, the advantages and disadvantages of each method are summarized, which can be helpful in future studies.

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Road Crack Detection using Support Vector Machine (SVM) and OTSU Algorithm

Cited by 45 publications

References 15 publications

Application of neural network method for road crack detection

Application of neural network method for road crack detection

Image-Based Crack Detection Methods: A Review

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

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