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Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis

This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To ad...

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Autores principales: Paramanandham, Nirmala, Rajendiran, Kishore, Poovathy J, Florence Gnana, Premanand, Yeshwant Santhanakrishnan, Mallichetty, Sanjeeve Raveenthiran, Kumar, Pramod
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10059214/
https://www.ncbi.nlm.nih.gov/pubmed/36991664
http://dx.doi.org/10.3390/s23062954
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author Paramanandham, Nirmala
Rajendiran, Kishore
Poovathy J, Florence Gnana
Premanand, Yeshwant Santhanakrishnan
Mallichetty, Sanjeeve Raveenthiran
Kumar, Pramod
author_facet Paramanandham, Nirmala
Rajendiran, Kishore
Poovathy J, Florence Gnana
Premanand, Yeshwant Santhanakrishnan
Mallichetty, Sanjeeve Raveenthiran
Kumar, Pramod
author_sort Paramanandham, Nirmala
collection PubMed
description This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To address the noise issue and to classify the cracks based on the severity level, a novel technique is proposed using a pixel-intensity resemblance measurement (PIRM) rule. Using PIRM, the noisy images and noiseless images were classified. Then, the noise was filtered using a median filter. The cracks were detected using VGG-16, ResNet-50 and InceptionResNet-V2 models. Once the crack was detected, the images were then segregated using a crack risk-analysis algorithm. Based on the severity level of the crack, an alert can be given to the authorized person to take the necessary action to avoid major accidents. The proposed technique achieved a 6% improvement without PIRM and a 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed 3 and 10% for ResNet-50, 2 and 3% for Inception ResNet and a 9 and 10% increment for the Xception model. When the images were corrupted from a single noise alone, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise.
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spelling pubmed-100592142023-03-30 Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis Paramanandham, Nirmala Rajendiran, Kishore Poovathy J, Florence Gnana Premanand, Yeshwant Santhanakrishnan Mallichetty, Sanjeeve Raveenthiran Kumar, Pramod Sensors (Basel) Article This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To address the noise issue and to classify the cracks based on the severity level, a novel technique is proposed using a pixel-intensity resemblance measurement (PIRM) rule. Using PIRM, the noisy images and noiseless images were classified. Then, the noise was filtered using a median filter. The cracks were detected using VGG-16, ResNet-50 and InceptionResNet-V2 models. Once the crack was detected, the images were then segregated using a crack risk-analysis algorithm. Based on the severity level of the crack, an alert can be given to the authorized person to take the necessary action to avoid major accidents. The proposed technique achieved a 6% improvement without PIRM and a 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed 3 and 10% for ResNet-50, 2 and 3% for Inception ResNet and a 9 and 10% increment for the Xception model. When the images were corrupted from a single noise alone, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise. MDPI 2023-03-08 /pmc/articles/PMC10059214/ /pubmed/36991664 http://dx.doi.org/10.3390/s23062954 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Paramanandham, Nirmala
Rajendiran, Kishore
Poovathy J, Florence Gnana
Premanand, Yeshwant Santhanakrishnan
Mallichetty, Sanjeeve Raveenthiran
Kumar, Pramod
Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis
title Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis
title_full Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis
title_fullStr Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis
title_full_unstemmed Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis
title_short Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis
title_sort pixel intensity resemblance measurement and deep learning based computer vision model for crack detection and analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10059214/
https://www.ncbi.nlm.nih.gov/pubmed/36991664
http://dx.doi.org/10.3390/s23062954
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