{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:13:46Z","timestamp":1760746426543,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhongyuan Science and Technology Innovation Leadership Talent Programme","award":["254200510043"],"award-info":[{"award-number":["254200510043"]}]},{"name":"National Scientific and Technological Innovation Teams of Universities in Henan Province","award":["25IRTSTHN018"],"award-info":[{"award-number":["25IRTSTHN018"]}]},{"name":"Key Research and Development Project of Henan Province","award":["241111110200"],"award-info":[{"award-number":["241111110200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In the automated cutting process of pork carcasses, asymmetric cutting path planning is critical. However, various substances on the carcass surface, such as blood stains and fascia, severely interfere with the separation boundaries between fresh meat and bones, significantly reducing the accuracy of asymmetric cutting path planning. To address these issues, this paper proposes a method for generating and fitting optimized cutting paths for pork carcasses (PGF-Net). Specifically, this method comprises a cutting path generation module that integrates multi-scale boundary features and a cutting path fitting optimization module. The cutting path generation module extracts asymmetric boundary information by enhancing attention to boundaries across different regions, identifies key cutting points, and generates a coarse cutting path. The cutting path fitting optimization module then performs fitting optimization on the generated key cutting points to ultimately produce a refined asymmetric cutting path. Experimental results demonstrate that PGF-Net achieves mean root mean square errors of 0.4212 cm, 0.4651 cm, and 0.5313 cm across three cutting paths on six different pork carcass images. Findings confirm that this method enhances the yield of premium meat cuts while reducing tool wear costs. It provides an innovative technological solution for automated meat processing, holding significant industrial application value.<\/jats:p>","DOI":"10.3390\/sym17101757","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T07:33:50Z","timestamp":1760686430000},"page":"1757","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PGF-Net: A Symmetric Cutting Path Generation and Fitting Optimization Method for Pig Carcasses Under Multi-Medium Interference"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4811-5854","authenticated-orcid":false,"given":"Lei","family":"Cai","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang 453000, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1633-3756","authenticated-orcid":false,"given":"Jin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453000, China"}]},{"given":"Pengtao","family":"Ban","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100331","DOI":"10.1016\/j.animal.2021.100331","article-title":"Production factors affecting poultry carcass and meat quality attributes","volume":"16","author":"Guillier","year":"2022","journal-title":"Animal"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.tifs.2023.03.018","article-title":"Robotization and intelligent digital systems in the meat cutting industry: From the perspectives of robotic cutting, perception, and digital development","volume":"135","author":"Xu","year":"2023","journal-title":"Trends Food Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.tifs.2020.11.005","article-title":"Robotisation and intelligent systems in abattoirs","volume":"108","author":"From","year":"2021","journal-title":"Trends Food Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109738","DOI":"10.1016\/j.meatsci.2024.109738","article-title":"Meat yields and primal cut weights from beef carcasses can be predicted with similar accuracies using in-abattoir 3D measurements or EUROP classification grade","volume":"222","author":"Nisbet","year":"2025","journal-title":"Meat Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1093\/jas\/sky418","article-title":"A novel automated system to acquire biometric and morphological measurements and predict body weight of pigs via 3D computer vision","volume":"97","author":"Fernandes","year":"2019","journal-title":"J. Anim. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/TMECH.2022.3218529","article-title":"Color machine vision design methodology of a part-presentation algorithm for automated poultry handling","volume":"28","author":"Lu","year":"2022","journal-title":"IEEE\/ASME Trans. Mech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109325","DOI":"10.1016\/j.meatsci.2023.109325","article-title":"Compact imaging system and deep learning based segmentation for objective longissimus muscle area in Korean beef carcass","volume":"206","author":"Yu","year":"2023","journal-title":"Meat Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10846-020-01280-3","article-title":"A porcine abdomen cutting robot system using binocular vision techniques based on kernel principal component analysis","volume":"101","author":"Cong","year":"2021","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Afonso, J.J., Almeida, M., Batista, A.C., Guedes, C., Teixeira, A., Silva, S., and Santos, V. (2024). Using Image Analysis Technique for Predicting Light Lamb Carcass Composition. Animals, 14.","DOI":"10.3390\/ani14111593"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105075","DOI":"10.1016\/j.compag.2019.105075","article-title":"Visual features based automated identification of fish species using deep convolutional neural networks","volume":"167","author":"Rauf","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","first-page":"8847984","article-title":"A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet","volume":"2021","author":"Zhao","year":"2021","journal-title":"J. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103765","DOI":"10.1016\/j.psj.2024.103765","article-title":"CarcassFormer: An end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect","volume":"103","author":"Tran","year":"2024","journal-title":"Poult. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ban, P., Cai, L., and Ma, H. (2024, January 6\u20138). FSRFS-Net: Fusion of Contour Features and Semantic Information for Rib Segmentation of Porcine Spine. Proceedings of the 2024 International Conference on Advanced Robotics and Mechatronics (ICARM), Tokyo, Japan.","DOI":"10.1109\/ICARM62033.2024.10715881"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1109\/TMECH.2021.3129911","article-title":"Robotic 3-D laser-guided approach for efficient cutting of porcine belly","volume":"27","author":"Liu","year":"2021","journal-title":"IEEE\/ASME Trans. Mech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.13031\/aea.14756","article-title":"Kinematics Analysis and Trajectory Planning of Segmentation Robot for Chilled Sheep Carcass","volume":"37","author":"Bao","year":"2021","journal-title":"Appl. Eng. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.fbp.2025.03.004","article-title":"Raw meat 3D laser scanning imaging: Optimized by adaptive contour unit","volume":"151","author":"Bu","year":"2025","journal-title":"Food Bioprod. Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Niu, H., and Cai, L. (2023, January 6\u20138). Segmentation Line Construction Method for Pig Carcass Musculature Based on Blurred and Distorted X-ray Images. Proceedings of the 2023 6th International Conference on Image and Graphics Processing, Chongqing, China.","DOI":"10.1145\/3582649.3582659"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1108\/IR-11-2023-0274","article-title":"Online path planning of pork cutting robot using 3D laser point cloud","volume":"51","author":"Liu","year":"2024","journal-title":"Ind. Robot. Int. J. Robot. Res. Appl."},{"key":"ref_19","first-page":"100318","article-title":"Smart agricultural technology","volume":"5","author":"Balkrishna","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, X., and Cai, L. (2023, January 6\u20138). Reinforced meta-learning method for shape-dependent regulation of cutting force in pork carcass operation robots. Proceedings of the 2023 6th International Conference on Image and Graphics Processing, Chongqing, China.","DOI":"10.1145\/3582649.3582665"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106964","DOI":"10.1016\/j.compag.2022.106964","article-title":"Visual navigation path extraction of orchard hard pavement based on scanning method and neural network","volume":"197","author":"Yang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108469","DOI":"10.1016\/j.compag.2023.108469","article-title":"Autonomous navigation method of jujube catch-and-shake harvesting robot based on convolutional neural networks","volume":"215","author":"Zheng","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, L., Wang, X., Hou, Z., Du, Z., Zeng, Y., and Mu, Z. (2021). Path planning optimization for driverless vehicle in parallel parking integrating radial basis function neural network. Appl. Sci., 11.","DOI":"10.3390\/app11178178"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108259","DOI":"10.1016\/j.ast.2023.108259","article-title":"Entry trajectory optimization for hypersonic vehicles based on convex programming and neural network","volume":"137","author":"Dai","year":"2023","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Molina-Leal, A., G\u00f3mez-Espinosa, A., Escobedo Cabello, J.A., Cuan-Urquizo, E., and Cruz-Ram\u00edrez, S.R. (2021). Trajectory planning for a mobile robot in a dynamic environment using an LSTM neural network. Appl. Sci., 11.","DOI":"10.3390\/app112210689"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, Z., Yue, W., Huang, J., and Wan, J. (2023). Ship trajectory prediction based on the TTCN-attention-GRU model. Electronics, 12.","DOI":"10.3390\/electronics12122556"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7887","DOI":"10.1109\/TMM.2024.3372835","article-title":"Boundary-guided lightweight semantic segmentation with multi-scale semantic context","volume":"26","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102634","DOI":"10.1016\/j.inffus.2024.102634","article-title":"CSWin-UNet: Transformer UNet with cross-shaped windows for medical image segmentation","volume":"113","author":"Liu","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Heidari, M., Kazerouni, A., Soltany, M., Azad, R., Aghdam, E.K., Cohen-Adad, J., and Merhof, D. (2023, January 2\u20137). Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV56688.2023.00614"},{"key":"ref_30","unstructured":"Ma, J., Li, F., and Wang, B. (2024). U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1757\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T07:53:57Z","timestamp":1760687637000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":30,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["sym17101757"],"URL":"https:\/\/doi.org\/10.3390\/sym17101757","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,17]]}}}