{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T13:29:46Z","timestamp":1769606986411,"version":"3.49.0"},"reference-count":16,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T00:00:00Z","timestamp":1743206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In this study, we proposed a method for detecting chips in the mouth of glass bottles using machine learning. In recent years, Japanese cosmetic glass bottles have gained attention for their advancements in manufacturing technology and eco-friendliness through the use of recycled glass, leading to an increase in the volume of glass bottle exports overseas. Although cosmetic bottles are subject to strict quality inspections from the standpoint of safety, the complicated shape of the glass bottle mouths makes automated inspections difficult, and visual inspections have been the norm. Visual inspections conducted by workers have become problematic because it has become clear that the standard of judgment differs from worker to worker and that inspection accuracy deteriorates after long hours of work. To address these issues, the development of inspection systems for glass bottles using image processing and machine learning has been actively pursued. While conventional image processing methods can detect chips in glass bottles, the target glass bottles are those without screw threads, and the light from the light source is diffusely reflected by the screw threads in the glass bottles in this study, resulting in a loss of accuracy. Additionally, machine learning-based inspection methods are generally limited to the body and bottom of the bottle, excluding the mouth from analysis. To overcome these challenges, this study proposed a method to extract only the screw thread regions from the bottle image, using a dedicated machine learning model, and perform defect detection. To evaluate the effectiveness of the proposed approach, accuracy was assessed by training models using images of both the entire mouth and just the screw threads. Experimental results showed that the accuracy of the model trained using the image of the entire mouth was 98.0%, while the accuracy of the model trained using the image of the screw threads was 99.7%, indicating that the proposed method improves the accuracy by 1.7%. In a demonstration experiment using data obtained at a factory, the accuracy of the model trained using images of the entire mouth was 99.7%, whereas the accuracy of the model trained using images of screw threads was 100%, indicating that the proposed system can be used to detect chips in factories.<\/jats:p>","DOI":"10.3390\/jimaging11040105","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T01:59:36Z","timestamp":1743386376000},"page":"105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Inspection of Defective Glass Bottle Mouths Using Machine Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Daiki","family":"Tomita","sequence":"first","affiliation":[{"name":"Setagaya Campas, Tokyo City University, Tokyo 158-8557, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Bao","sequence":"additional","affiliation":[{"name":"Setagaya Campas, Tokyo City University, Tokyo 158-8557, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,29]]},"reference":[{"key":"ref_1","unstructured":"Japan Glass Bottle Association (2024, September 27). Returnable Bottle. Available online: https:\/\/glassbottle.org\/ecology\/returnable\/."},{"key":"ref_2","unstructured":"Japan Cosmetic Industry Association (2024, September 27). Cosmetics Statistics. Available online: https:\/\/www.jcia.org\/user\/statistics\/trade."},{"key":"ref_3","unstructured":"Ma, H.M., Su, G.D., Wang, J.Y., and Ni, Z. (2002, January 4\u20135). A glass bottle defect detection system without touching. Proceedings of the Proceedings. International Conference on Machine Learning and Cybernetics, Beijing, China."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Latina, M.A., John Van Russel, R., and Santos, F.D. (2022, January 1\u20134). Empty Glass Bottle Defect Detection Based on Deep Learning with CNN Using SSD MobileNetV2 Model. Proceedings of the 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines.","DOI":"10.1109\/HNICEM57413.2022.10109368"},{"key":"ref_5","first-page":"454","article-title":"Effect of object handling on accuracy of visual inspection","volume":"48","author":"Hida","year":"2012","journal-title":"Proc. Annu. Conf. Jpn. Ergon. Soc."},{"key":"ref_6","unstructured":"Harada, T., and Suzuki, G. (2011). Container Mouth Inspection Method and Device. (4986255), Patent No."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tomita, D., and Bao, Y. (2025). Detection of Chips on the Threaded Part of Cosmetic Glass Bottles. J. Imaging, 11.","DOI":"10.3390\/jimaging11030077"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gulzar, Y. (2023). Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique. Sustainability, 15.","DOI":"10.3390\/su15031906"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1007\/s00170-022-10386-x","article-title":"Intelligent surface defect detection for submersible pump impeller using MobileNet V2 architecture","volume":"124","author":"Sambandam","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_10","unstructured":"OpenCV (2024, September 27). Structural Analysis and Shape Descriptors. Available online: https:\/\/docs.opencv.org\/3.4\/d3\/dc0\/group__imgproc__shape.html."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1109\/TIP.2019.2946979","article-title":"Spaghetti Labeling: Directed Acyclic Graphs for Block-Based Connected Components Labeling","volume":"29","author":"FBolelli","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","first-page":"84","article-title":"Imagenet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/4\/105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:05:27Z","timestamp":1760029527000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/4\/105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,29]]},"references-count":16,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["jimaging11040105"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11040105","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,29]]}}}