{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T05:10:34Z","timestamp":1789017034272,"version":"build-2803163510"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["MOST 108-2221-E-324-008"],"award-info":[{"award-number":["MOST 108-2221-E-324-008"]}]},{"award":["MOST 108-2221-E-324-008"],"award-info":[{"award-number":["MOST 108-2221-E-324-008"]}],"id":[{"id":"https:\/\/ror.org\/02kv4zf79","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automated inspection of prepared meals is important for improving food safety, quality assurance, and production efficiency. However, vision-based inspection remains challenging because boxed meals contain multiple adjacent food items with irregular shapes, varying portion sizes, and visually similar appearances, while oily surfaces may introduce specular reflections that degrade image quality. This study presents a vision-based framework for multi-object recognition and quantitative defect analysis in complex meal images, with Chinese-style lunch boxes used as representative test samples. The framework integrates region-of-interest (ROI) extraction, fixed-grid regional feature representation, deep neural network (DNN) classification, flood-filling post-processing, and empirical food-quantity thresholds. The lunch-box ROI is first extracted using the Hough transform, followed by median filtering to suppress reflection noise. The ROI is partitioned into 6 \u00d7 6 regions, from which the mean and standard deviation of RGB, HSV, and CIE Lab* color components are extracted and classified using a DNN. Flood filling is subsequently applied to refine the classification results, and category-specific empirical thresholds are used to identify missing food items and insufficient portions. Foreign objects are detected as an additional abnormal category. Under the evaluated experimental conditions, the proposed framework achieved an overall image classification rate (CR) of 96.45%, a defective-image detection rate (1\u2212\u03b2) of 97.76%, a normal-image false alarm rate (\u03b1) of 5.34%, and a defective-image misclassification rate (\u03b3) of 0.82%. Sensitivity experiments involving illumination variation, two lunch-box configurations with different food compositions, and conveyor-based image acquisition further demonstrated the feasibility and stability of the framework under the evaluated laboratory and prototype conditions. These findings support the feasibility of the proposed lightweight framework for vision-based quality inspection of representative boxed meals, while broader validation across meal types, contaminants, and industrial production environments remains necessary.<\/jats:p>","DOI":"10.3390\/s26175636","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T17:25:56Z","timestamp":1788542756000},"page":"5636","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Vision-Based Automated Inspection of Box Meals for Food Portion Defects and Foreign Object Detection"],"prefix":"10.3390","volume":"26","author":[{"given":"Hong-Dar","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guan-Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chou-Hsien","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712-0273, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0168-1699(02)00101-1","article-title":"Inspection and grading of agricultural and food products by computer vision systems-a review","volume":"36","author":"Brosnan","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0260-8774(03)00183-3","article-title":"Improving quality inspection of food products by computer vision-A review","volume":"61","author":"Brosnan","year":"2004","journal-title":"J. Food Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s13197-011-0321-4","article-title":"Machine vision system: A tool for quality inspection of food and agricultural products","volume":"49","author":"Patel","year":"2012","journal-title":"J. Food Sci. Technol."},{"key":"ref_4","unstructured":"Sun, D.-W. (2008). Computer Vision Technology for Food Quality Evaluation, Academic Press."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.tifs.2012.08.004","article-title":"Colour measurements by computer vision for food quality control-A review","volume":"29","author":"Wu","year":"2013","journal-title":"Trends Food Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1111\/1541-4337.12492","article-title":"Application of deep learning in food: A review","volume":"18","author":"Zhou","year":"2019","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.crfs.2021.03.009","article-title":"Deep learning and machine vision for food processing: A survey","volume":"4","author":"Zhu","year":"2021","journal-title":"Curr. Res. Food Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12393-023-09363-1","article-title":"Applications of artificial intelligence and machine learning in food quality control and safety assessment","volume":"16","author":"Chhetri","year":"2024","journal-title":"Food Eng. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., and Ma, Y. (2016). Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment. International Conference on Smart Homes and Health Telematics, Springer International Publishing.","DOI":"10.1007\/978-3-319-39601-9_4"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1109\/LSP.2017.2758862","article-title":"FoodNet: Recognizing foods using ensemble of deep networks","volume":"24","author":"Pandey","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tahir, G.A., and Loo, C.K. (2021). A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment. Healthcare, 9.","DOI":"10.3390\/healthcare9121676"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s42979-023-01972-1","article-title":"Deep learning for food image recognition and nutrition analysis: Towards chronic diseases monitoring-A systematic review","volume":"4","author":"Mansouri","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref_13","first-page":"15","article-title":"Review of the deep learning for food image processing","volume":"17","author":"Niu","year":"2024","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compbiomed.2016.07.006","article-title":"Retrieval and classification of food images","volume":"77","author":"Farinella","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.procs.2017.10.020","article-title":"Machine learning and SIFT approach for Indonesian food image recognition","volume":"116","author":"Giovany","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1016\/j.patcog.2013.12.006","article-title":"A mobile, lightweight, poll-based food identification system","volume":"47","author":"Oliveira","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/JBHI.2016.2636441","article-title":"Food recognition: A new dataset, experiments, and results","volume":"21","author":"Ciocca","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Matsuda, Y., Hoashi, H., and Yanai, K. (2012, January 9\u201313). Recognition of multiple-food images by detecting candidate regions. Proceedings of the 2012 IEEE International Conference on Multimedia and Expo, Melbourne, VIC, Australia.","DOI":"10.1109\/ICME.2012.157"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/JBHI.2014.2304925","article-title":"Multiple hypotheses image segmentation and classification with application to dietary assessment","volume":"19","author":"Zhu","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chopra, M., and Purwar, A. (2022). Food image recognition using CNN, faster R-CNN and YOLO. Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development, CRC Press.","DOI":"10.1201\/9781003245469-6"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sudharson, S., Kokil, P., Annamalai, R., and Manoj, N.S. (2023, January 6\u20138). Enhanced food classification system using YOLO models for object detection algorithm. Proceedings of the 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India.","DOI":"10.1109\/ICCCNT56998.2023.10307943"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Berretti, S., and Azari, H. (2025). Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module. Smart Multimedia, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-82475-3"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"12882","DOI":"10.1109\/JSEN.2020.3041023","article-title":"A systematic review of sensor-based methodologies for food portion size estimation","volume":"21","author":"Raju","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_24","first-page":"1180","article-title":"Image-based food portion size estimation using a smartphone without a fiducial marker","volume":"22","author":"Yang","year":"2019","journal-title":"Public Health Nutr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vinod, G., He, J., Shao, Z., and Zhu, F. (2024, January 17\u201318). Food portion estimation via 3D object scaling. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPRW63382.2024.00378"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fang, S., Zhu, F., Kerr, D.A., Boushey, C.J., and Delp, E.J. (2018, January 7\u201310). Single-view food portion estimation: Learning image-to-energy mappings using generative adversarial networks. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451461"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1109\/JSEN.2023.3235956","article-title":"Estimation of plate and bowl dimensions for food portion size assessment in a wearable sensor system","volume":"23","author":"Raju","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e12808","DOI":"10.1111\/jfpe.12808","article-title":"Noninvasive techniques for detection of foreign bodies in food: A review","volume":"41","author":"Ibrahim","year":"2018","journal-title":"J. Food Process Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s10921-025-01203-y","article-title":"Research on X-ray Image Fusion Algorithm for Food Foreign Object Detection","volume":"44","author":"Yao","year":"2025","journal-title":"J. Nondestruct. Eval."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.compag.2019.05.019","article-title":"Computer vision detection of foreign objects in walnuts using deep learning","volume":"162","author":"Rong","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Son, G.J., Kwak, D.H., Park, M.K., Kim, Y.D., and Jung, H.C. (2021). U-Net-based foreign object detection method using effective image acquisition system: A case of almond and green onion flake food process. Sustainability, 13.","DOI":"10.3390\/su132413834"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Wang, J., Han, L., Guo, S., and Cui, Q. (2022). Application of machine vision system in food detection. Front. Nutr., 9.","DOI":"10.3389\/fnut.2022.888245"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1080\/10408398.2022.2131725","article-title":"Applications of machine learning techniques for enhancing nondestructive food quality and safety detection","volume":"63","author":"Lin","year":"2023","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5262294","DOI":"10.1155\/2022\/5262294","article-title":"Food quality inspection and grading using efficient image segmentation and machine learning-based system","volume":"2022","author":"Hemamalini","year":"2022","journal-title":"J. Food Qual."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"PAMI-8","author":"Canny","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/361237.361242","article-title":"Use of the Hough transformation to detect lines and curves in pictures","volume":"15","author":"Duda","year":"1972","journal-title":"Commun. ACM"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"Gonzalez, R.C., and Woods, R.E. (2018). Digital Image Processing, Pearson. [4th ed.]."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Glassner, A.S. (1990). Seed fill algorithms. Graphics Gems, Academic Press.","DOI":"10.1016\/B978-0-08-050753-8.50133-9"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1109\/TSMCC.2008.919172","article-title":"Pareto-based multiobjective machine learning: An overview and case studies","volume":"38","author":"Jin","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5636\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T04:35:13Z","timestamp":1789014913000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5636"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,4]]},"references-count":40,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175636"],"URL":"https:\/\/doi.org\/10.3390\/s26175636","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,4]]}}}