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In this study, an intelligent detection system for automotive wheel defects is also designed, and finally the performance of the detection system is tested experimentally to illustrate its practicality. The experimental results show that the proposed intelligent detection system for automotive wheel defects based on image texture features identifies defects in wheel castings with a correct rate of 96% and a false positive rate of only 2%. This illustrates that the detection system proposed in this study has a high recognition rate and can provide a useful reference for the automotive industry inspection.<\/jats:p>","DOI":"10.3233\/jcm-226789","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T15:25:47Z","timestamp":1687274747000},"page":"1941-1953","source":"Crossref","is-referenced-by-count":0,"title":["Damage detection method of automobile hub based on image texture feature"],"prefix":"10.1177","volume":"23","author":[{"given":"Ying","family":"Wang","sequence":"first","affiliation":[]}],"member":"179","reference":[{"issue":"11","key":"10.3233\/JCM-226789_ref1","doi-asserted-by":"crossref","first-page":"5117","DOI":"10.1007\/s12206-021-1028-8","article-title":"A feasible strain-history extraction method using machine learning for the durability evaluation of automotive parts","volume":"35","author":"Jang","year":"2021","journal-title":"J Mech Sci Technol."},{"issue":"4","key":"10.3233\/JCM-226789_ref2","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1007\/s13349-021-00509-5","article-title":"Structural damage identification using modified Hilbert-Huang transform and support vector machine","volume":"11","author":"Diao","year":"2021","journal-title":"J Civil Struct Health Monit."},{"issue":"6","key":"10.3233\/JCM-226789_ref3","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.21595\/jve.2019.20858","article-title":"Structural nonlinear damage detection using improved Dempster-Shafer theory and time domain model","volume":"21","author":"Guo","year":"2019","journal-title":"J Vibroeng."},{"key":"10.3233\/JCM-226789_ref4","doi-asserted-by":"crossref","unstructured":"Wang X, Li L, Beck JL. 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