{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:51:45Z","timestamp":1760237505897,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan Project of Hebei Province, China","award":["16211803D"],"award-info":[{"award-number":["16211803D"]}]},{"name":"Tianjin Municipal Natural Science Foundation","award":["18JCZDJC40100"],"award-info":[{"award-number":["18JCZDJC40100"]}]},{"name":"The Program for Innovative Research Team in University of Tianjin","award":["TD13-5037"],"award-info":[{"award-number":["TD13-5037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Various defects are formed on the workpiece surface during the production process. Workpiece surface defects are classified according to various characteristics, which includes a bumped surface, scratched surface and pit surface. Suppliers analyze the cause of workpiece surface defects through the defect types and thus determines the subsequent processing. Therefore, the correct classification is essential regarding workpiece surface defects. In this paper, a multi-classifier decision-level fusion classification model for workpiece surface defects based on a convolutional neural network (CNN) was proposed. In the proposed model, the histogram of oriented gradient (HOG) was used to extract the features of the second fully connected layer of the CNN, and the features of the HOG were further extracted by using the local binary patterns (LBP), which was called the HOG\u2013LBP feature extraction. Finally, this paper designed a symmetry ensemble classifier, which was used to classify the features of the last fully connected layer of the CNN and the features of the HOG\u2013LBP. The comprehensive decision was made by fusing the classification results of the symmetry structure channels. The experiments were carried out, and the results showed that the proposed model could improve the accuracy of the workpiece surface defect classification.<\/jats:p>","DOI":"10.3390\/sym12050867","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T11:42:02Z","timestamp":1590406922000},"page":"867","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-Classifier Decision-Level Fusion Classification of Workpiece Surface Defects Based on a Convolutional Neural Network"],"prefix":"10.3390","volume":"12","author":[{"given":"Fen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Tiangong University, NO. 399 Binshui West Road, Xiqing District, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1168-6645","authenticated-orcid":false,"given":"Yuxuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Tiangong University, NO. 399 Binshui West Road, Xiqing District, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongqiang","family":"Sang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Tiangong University, NO. 399 Binshui West Road, Xiqing District, Tianjin 300387, China"},{"name":"Tianjin Key Laboratory of Advanced Mechatronic Equipment Technology, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.jmsy.2017.10.004","article-title":"Automated defect inspection system for CMOS image sensor with micro multi-layer non-spherical lens module","volume":"45","author":"Jeffrey","year":"2017","journal-title":"J. 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