A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks
Ali Mohsin Al-juboori,
Ali Hakem Alsaeedi,
Riyadh Rahef Nuiaa,
Zaid Abdi Alkareem Alyasseri,
Nor Samsiah Sani,
Suha Mohammed Hadi,
Husam Jasim Mohammed,
Bashaer Abbuod Musawi,
Maifuza Mohd Amin
Affiliations
Ali Mohsin Al-juboori
College of Computer Science and Information Technology, Al-Qadisiyah University, Diwaniyah 58009, Iraq
Ali Hakem Alsaeedi
College of Computer Science and Information Technology, Al-Qadisiyah University, Diwaniyah 58009, Iraq
Riyadh Rahef Nuiaa
Department of Computer, College of Education for Pure Sciences, Wasit University, Wasit 52001, Iraq
Zaid Abdi Alkareem Alyasseri
Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf 54002, Iraq
Nor Samsiah Sani
Center for Artificial Intelligence Technology, Faculty of Information Science &Technology, University Kebangsaan Malaysia, Bangi 43600, Selangor Darul Ehsan, Malaysia
Suha Mohammed Hadi
Informatics Institute for Postgraduate Studies, Iraqi Commission for Computer and Informatics, Baghdad 10001, Iraq
Husam Jasim Mohammed
Department of Business Administration, College of Administration and Financial Sciences, Imam Ja’afar Al-Sadiq University, Baghdad 10001, Iraq
Bashaer Abbuod Musawi
Department of Biology, Faculty of Education for Girls, University of Kufa, Najaf 54001, Iraq
Maifuza Mohd Amin
Center for Artificial Intelligence Technology, Faculty of Information Science &Technology, University Kebangsaan Malaysia, Bangi 43600, Selangor Darul Ehsan, Malaysia
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%).
