{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:01:22Z","timestamp":1786122082652,"version":"build-2736575974"},"reference-count":28,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32372434"],"award-info":[{"award-number":["32372434"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["32372434"],"award-info":[{"award-number":["32372434"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Liaoning Provincial Department of Education","award":["LJ212410152024"],"award-info":[{"award-number":["LJ212410152024"]}]},{"name":"Shandong Province Science and Technology-based Small and Medium-sized Enterprises Innovation Capacity Enhancement Project","award":["2025TSGCCZZB0061"],"award-info":[{"award-number":["2025TSGCCZZB0061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>Chicken meat holds a dominant position in the meat consumption market, and broilers are important primary processed products. However, during the slaughtering and processing of broilers, steps such as hanging, bleeding, scalding, defeathering, neck slitting, and evisceration can easily cause skin break and skin scratch on chicken carcasses due to manual operational errors. Additionally, chicken carcasses may suffer broken wing bones from intense collisions during transportation. These issues negatively impact the quality and market acceptance of broilers in subsequent sales. This study aims to develop a stable and efficient method that leverages the unique technical advantages of hyperspectral imaging information fusion and rich dimensionality. By capturing, processing, and fusing multi-scale information, the method achieves automatic detection of defects such as skin break, skin scratch, and exposed broken bones during chicken carcass processing. The improved defect detection model for chicken carcasses shows significantly enhanced performance, with average precision (AP) exceeding 92% and recall rates above 91% for all defects. The overall mean average precision (mAP) increased from 80.2% to 93.6%. This method not only provides a reliable technical approach for detecting defects in chicken carcasses but also offers a referable solution for the visual inspection of other livestock and poultry products.<\/jats:p>","DOI":"10.3390\/foods15162775","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T08:01:36Z","timestamp":1786089696000},"page":"2775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hyperspectral Imaging Combined with Multi-Scale Feature Fusion and Improved Faster R-CNN for Automated Detection of Surface Defects on Chicken Carcasses"],"prefix":"10.3390","volume":"15","author":[{"given":"Huihui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China"},{"name":"National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China"},{"name":"State Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian 116034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"He","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China"},{"name":"National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China"},{"name":"State Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian 116034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangyi","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanshan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenkai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China"},{"name":"National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China"},{"name":"State Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian 116034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101072","DOI":"10.1016\/j.psj.2021.101072","article-title":"Weight and volume estimation of poultry and products based on computer vision systems: A review","volume":"100","author":"Nyalala","year":"2021","journal-title":"Poult. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101474","DOI":"10.1016\/j.psj.2021.101474","article-title":"On-line weight estimation of broiler carcass and cuts by a computer vision system","volume":"100","author":"Nyalala","year":"2021","journal-title":"Poult. Sci."},{"key":"ref_3","first-page":"7097","article-title":"Analysis of a poultry slaughter process: Influence of process stages on the microbiological contamination of broiler carcasses","volume":"6","author":"Althaus","year":"2017","journal-title":"Ital. J. Food Saf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.meatsci.2012.08.010","article-title":"Measurement of meat color using a computer vision system","volume":"93","author":"Girolami","year":"2013","journal-title":"Meat Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, H.-W., Chen, C.-H., Tsai, Y.-C., Hsieh, K.-W., and Lin, H.-T. (2021). Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm. Sensors, 21.","DOI":"10.3390\/s21113579"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.compag.2016.03.011","article-title":"Weight prediction of broiler chickens using 3D computer vision","volume":"123","author":"Mortensen","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","first-page":"3363","article-title":"A dual-band algorithm to detect contaminants with low visibility on chicken carcass surface","volume":"34","author":"Wu","year":"2014","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Signoroni, A., Savardi, M., Baronio, A., and Benini, S. (2019). Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review. J. Imaging, 5.","DOI":"10.3390\/jimaging5050052"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Al-Sarayreh, M., M. Reis, M., Qi Yan, W., and Klette, R. (2018). Detection of Red-Meat Adulteration by Deep Spectral\u2013Spatial Features in Hyperspectral Images. J. Imaging, 4.","DOI":"10.3390\/jimaging4050063"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Campos, R.L., Yoon, S.-C., Chung, S., and Bhandarkar, S.M. (2023). Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging. Sensors, 23.","DOI":"10.3390\/s23167014"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108559","DOI":"10.1016\/j.microc.2023.108559","article-title":"Distinguishing fresh and frozen-thawed beef using hyperspectral imaging technology combined with convolutional neural networks","volume":"189","author":"Pu","year":"2023","journal-title":"Microchem. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e14304","DOI":"10.1111\/jfpe.14304","article-title":"Detection of starch in minced chicken meat based on hyperspectral imaging technique and transfer learning","volume":"46","author":"Yang","year":"2023","journal-title":"J. Food Process Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"012045","DOI":"10.1088\/1742-6596\/1528\/1\/012045","article-title":"Examination system of chicken meat quality based on hyperspectral imaging","volume":"1528","author":"Imawan","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral Band Selection: A Review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_16","unstructured":"Nieuwenhuizen, A., Hemming, J., and Suh, H.K. (2018). Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. The Netherlands Conference on Computer Vision, Wageningen University & Research."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gong, X., and Zhang, S. (2023). A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN. Agriculture, 13.","DOI":"10.3390\/agriculture13020240"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zheng, H., Zhao, N., Xu, S., He, J., Ospina, R., Qiu, Z., and Liu, Y. (2024). Deep Learning-Based Automated Cell Detection-Facilitated Meat Quality Evaluation. Foods, 13.","DOI":"10.3390\/foods13142270"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zu, K., Lu, J., Zou, Y., and Meng, D. (2022, January 4\u20138). EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network. Proceedings of the Computer Vision\u2014ACCV 2022: 16th Asian Conference on Computer Vision, Macao, China. Part III.","DOI":"10.1007\/978-3-031-26313-2_33"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"30685","DOI":"10.1007\/s11042-022-11940-1","article-title":"CE-FPN: Enhancing channel information for object detection","volume":"81","author":"Luo","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.foodres.2013.12.018","article-title":"Comparison of Visible\u2013Near Infrared and Short Wave Infrared hyperspectral imaging for the evaluation of rainbow trout freshness","volume":"56","author":"Khojastehnazhand","year":"2014","journal-title":"Food Res. Int."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1080\/09500340.2023.2248634","article-title":"Hyperspectral images classification for white blood cells with attention-aided convolutional neural networks and fusion network","volume":"70","author":"Shao","year":"2023","journal-title":"J. Mod. Opt."},{"key":"ref_24","first-page":"918","article-title":"Hyperspectral model optimization for protein of tan mutton based on Box-Behnken","volume":"41","author":"Fan","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.ifset.2012.06.003","article-title":"Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression","volume":"16","author":"Kamruzzaman","year":"2012","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_26","first-page":"3632","article-title":"Non-Destructive Detection of Ready-to-Eat Sea Cucumber Freshness Based on Hyperspectral Imaging","volume":"37","author":"Wang","year":"2017","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.lwt.2014.10.021","article-title":"Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats","volume":"60","author":"Xiong","year":"2015","journal-title":"LWT-Food Sci. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.ifset.2013.09.002","article-title":"Non-destructive prediction of salt contents and water activity of porcine meat slices by hyperspectral imaging in a salting process","volume":"20","author":"Liu","year":"2013","journal-title":"Innov. Food Sci. Emerg. Technol."}],"container-title":["Foods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/16\/2775\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T08:34:56Z","timestamp":1786091696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/16\/2775"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,7]]},"references-count":28,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["foods15162775"],"URL":"https:\/\/doi.org\/10.3390\/foods15162775","relation":{},"ISSN":["2304-8158"],"issn-type":[{"value":"2304-8158","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,7]]}}}