Staff View: A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways t...

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Main Authors:	Al-juboori A.M., Alsaeedi A.H., Nuiaa R.R., Alyasseri Z.A.A., Sani N.S., Hadi S.M., Mohammed H.J., Musawi B.A., Amin M.M.
Other Authors:	56071367500
Format:	Article
Published:	MDPI 2024
Subjects:	deep belief neural networks feature extraction feature selection safe driving tire defect detection
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id	my.uniten.dspace-34275
record_format	dspace
spelling	my.uniten.dspace-342752024-10-14T11:18:46Z A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks Al-juboori A.M. Alsaeedi A.H. Nuiaa R.R. Alyasseri Z.A.A. Sani N.S. Hadi S.M. Mohammed H.J. Musawi B.A. Amin M.M. 56071367500 57219177058 57226309117 57862594800 57196190931 57855381500 57202657688 57439487000 58124450400 deep belief neural networks feature extraction feature selection safe driving tire defect detection Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks� performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%). � 2023 by the authors. Final 2024-10-14T03:18:46Z 2024-10-14T03:18:46Z 2023 Article 10.3390/sym15020358 2-s2.0-85149235592 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149235592&doi=10.3390%2fsym15020358&partnerID=40&md5=22f4890dd29d67beb5bfb66c63a04956 https://irepository.uniten.edu.my/handle/123456789/34275 15 2 358 All Open Access Gold Open Access MDPI Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	deep belief neural networks feature extraction feature selection safe driving tire defect detection
spellingShingle	deep belief neural networks feature extraction feature selection safe driving tire defect detection Al-juboori A.M. Alsaeedi A.H. Nuiaa R.R. Alyasseri Z.A.A. Sani N.S. Hadi S.M. Mohammed H.J. Musawi B.A. Amin M.M. A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks
description	Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks� performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%). � 2023 by the authors.
author2	56071367500
author_facet	56071367500 Al-juboori A.M. Alsaeedi A.H. Nuiaa R.R. Alyasseri Z.A.A. Sani N.S. Hadi S.M. Mohammed H.J. Musawi B.A. Amin M.M.
format	Article
author	Al-juboori A.M. Alsaeedi A.H. Nuiaa R.R. Alyasseri Z.A.A. Sani N.S. Hadi S.M. Mohammed H.J. Musawi B.A. Amin M.M.
author_sort	Al-juboori A.M.
title	A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks
title_short	A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks
title_full	A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks
title_fullStr	A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks
title_full_unstemmed	A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks
title_sort	hybrid cracked tiers detection system based on adaptive correlation features selection and deep belief neural networks
publisher	MDPI
publishDate	2024
_version_	1814061113538510848
score	13.226497

A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

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