Development of Underwater Pipe Crack Detection System for Low-Cost Underwater Vehicle using Raspberry Pi and Canny Edge Detection Method

Aliff, Mohd; Hanisah, Nur Farah; Ashroff, Muhammad Shafique; Hassan, Sallaudin; Nurr, Siti Fairuz; Sani, Nor Samsiah

doi:10.14569/ijacsa.2022.0131152

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…In the era of Industry 4.0, technological advancements have permeated diverse sectors, revolutionized traditional practices, and unlocked unprecedented potentials [1][2][3]. In agriculture, the convergence of cutting-edge technologies such as microcontroller, the Internet of Things (IoT), Robotics, and Deep Learning has ushered in a new paradigm of smart farming [4,5].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Aliff,

Luqman,

Yusof

et al. 2024

ITM Web Conf.

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The primary agricultural pursuit in Malaysia centres around banana cultivation; however, this vital crop faces the daunting challenge of multiple diseases that hinder its growth. The adverse consequences of these diseases extend beyond the farms to impact the nation’s economy. To empower farmers with the tools to promptly identify and categorize these diseases, image processing techniques offer a valuable solution. This research leverages deep learning Convolutional Neural Networks (CNN) implemented through MATLAB in conjunction with a DJI drone. By harnessing this technology, the system can automatically detect and classify major banana diseases. The study meticulously fine-tuned several hyperparameters to achieve impressive training and testing accuracy levels. The results revealed that the model attained its highest training accuracy of 81.27% at epoch 8 and its lowest accuracy of 78.40% at epoch 4, demonstrating its potential to aid in early disease detection and classification in banana crops.

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Section: Introductionmentioning

confidence: 99%

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Aliff,

Luqman,

Yusof

et al. 2024

ITM Web Conf.

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“…Crack is a major defect of pipeline, and it will expand along the direction of crack, resulting in pipeline leakage and fracture accidents. Therefore, the detection of cracks has always been a major topic in the research of nondestructive testing technology [1][2][3]. At present, the research of crack length, area, depth and crack degree is mostly carried out under the condition that the crack direction is known, but the crack direction is unknown in practice, so the problem of crack direction has aroused widespread concern.…”

Section: Introductionmentioning

confidence: 99%

Crack orientation recognition method based on prototype learning

Liu

Chen

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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To address the problem that the direction of pipe cracks is difficult to detect, a crack direction recognition method based on prototype learning is proposed with a prototype network as a framework. First, through the convolutional layer of the prototype network, shallow features of crack directions are extracted to improve the generalization ability of the model on the data set of this paper. then, by improving the High-Resolution Network and introducing a location self-attention mechanism, and combined with a migration training method for the data set of this paper, a category that can accurately reflect the crack directions is constructed prototype learning mechanism. Finally, pattern recognition is performed by the metric classification methods, the effective classification of crack direction under small sample condition is achieved. The experimental results show that the recognition accuracy of the crack direction recognition method based on prototype learning can reach 99.2% with the sample parameters unchanged.

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“…However, assessing damage classification from the superficial visual information of reinforced concrete (RC) requires many years of experience and expertise. Other field investigations involving hazards are difficult to conduct [5,6]. In Japan, the Ministry of Land, Infrastructure, and Transport created an "Inspection Support Technology Performance Catalog" to provide inspection support technologies [7,8].…”

Section: Introductionmentioning

confidence: 99%

Development of Crack Detection and Crack Length Calculation Method using Image Processing

Oh,

Matsumoto,

Arai

2023

IJACSA

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To evaluate the integrity of a building, many experts and engineers have evaluated the damage classification of a building based on superficial visual information through field surveys. On-site surveys are hazardous and require several years of experience and expertise. In this study, a system for detecting the presence or absence of cracks and calculating their lengths was developed using image processing technology. The accuracy of the system was examined using crack image data obtained from shear force experiments. For crack detection, a crack detection method was developed using canny edge, threshold, and HSV color detection. The detection of the presence of cracks was proposed to be coupled with image segmentation to improve detection accuracy. A method for calculating the crack length using image processing was also developed. In this study, we proposed a method to calculate cracks as straight lengths, and obtained results with 98.1% accuracy. However, for curved cracks, it was necessary to rotate or segment the image.

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Development of Underwater Pipe Crack Detection System for Low-Cost Underwater Vehicle using Raspberry Pi and Canny Edge Detection Method

Cited by 4 publications

References 29 publications

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Crack orientation recognition method based on prototype learning

Development of Crack Detection and Crack Length Calculation Method using Image Processing

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