{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T16:19:20Z","timestamp":1786637960461,"version":"3.56.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Traditional methods for detecting surface defects on ceramic tiles result in misdetection and missed detection, which makes it difficult to guarantee product stability and consistency within the same batch. Therefore, this article proposes an improved YOLOv5 algorithm for detecting surface defects on ceramic tiles. Firstly, the Res2Net module is combined with self-attention to fully utilize local and global information and improve the feature extraction effect of defects. Secondly, the GS-BiFPN neck network is designed to enhance the fusion capability of shallow detail and deep semantic information and alleviate ambiguity and redundancy on the feature map. Then, a lightweight attention module is introduced to improve the detection capability of difficult-to-recognize defects and anti-background interference. Finally, the SIoU loss function improves the model\u2019s convergence speed and accuracy. Experimental results demonstrate that the improved algorithm\u2019s mean average precision (mAP) reaches 73.3%, 6.3% higher than the baseline model. Even when compared with YOLOv7-tiny, the mAP of the improved algorithm has increased by 8.7%. Additionally, the detection speed of the model can reach 92 frames per second, which can meet the requirements of ceramic tile surface defect detection in industrial scenarios.<\/jats:p>","DOI":"10.3233\/jifs-231991","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T11:42:25Z","timestamp":1696333345000},"page":"11319-11331","source":"Crossref","is-referenced-by-count":2,"title":["Research on tile surface defect detection by improved YOLOv5"],"prefix":"10.1177","volume":"45","author":[{"given":"Xulong","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"},{"name":"The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiancheng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"},{"name":"The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aoqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China"},{"name":"The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Business, North Minzu University, Yinchuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/JIFS-231991_ref1","doi-asserted-by":"crossref","first-page":"4389","DOI":"10.1016\/j.ceramint.2020.10.065","article-title":"Review of non-destructive testing methods for defect detection of ceramics","volume":"47","author":"Zhao","year":"2021","journal-title":"Ceramics International"},{"issue":"21","key":"10.3233\/JIFS-231991_ref2","doi-asserted-by":"crossref","first-page":"32113","DOI":"10.1016\/j.ceramint.2022.07.151","article-title":"Saliency detection for surface defects of ceramic tile","volume":"48","author":"Zhang","year":"2022","journal-title":"Ceramics International"},{"key":"10.3233\/JIFS-231991_ref3","first-page":"28","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","author":"Ren","year":"2015","journal-title":"Advances in Neural Information Processing 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