{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T01:16:57Z","timestamp":1783127817127,"version":"3.54.6"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Research Council of Norway","doi-asserted-by":"publisher","award":["313737"],"award-info":[{"award-number":["313737"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.compag.2026.111852","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T10:06:59Z","timestamp":1780135619000},"page":"111852","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FishKeypoints: A comprehensive stereo vision framework for fish behaviour monitoring and size measurement in aquaculture"],"prefix":"10.1016","volume":"250","author":[{"given":"Qin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Parmiggiani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomas","family":"Norton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9312-7443","authenticated-orcid":false,"given":"Martin","family":"F\u00f8re","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleni","family":"Kelasidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111852_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101847","article-title":"YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment","volume":"72","author":"Al Muksit","year":"2022","journal-title":"Ecol. Informatics"},{"key":"10.1016\/j.compag.2026.111852_b2","article-title":"A novel computer vision approach for assessing fish responses to intrusive objects in aquaculture","author":"Alvheim","year":"2026","journal-title":"Submitt. Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111852_b3","series-title":"Fisheries techniques","first-page":"447","article-title":"Length, weight, and associated structural indices","author":"Anderson","year":"1996"},{"issue":"1","key":"10.1016\/j.compag.2026.111852_b4","doi-asserted-by":"crossref","first-page":"3219","DOI":"10.1038\/s41598-021-81997-9","article-title":"Zebrafish tracking using YOLOv2 and Kalman filter","volume":"11","author":"Barreiros","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.111852_b5","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., Davis, L.S., 2017. Soft-NMS\u2013improving object detection with one line of code. In: Proceedings of the IEEE International Conference on Computer Vision. ICCV, pp. 5561\u20135569.","DOI":"10.1109\/ICCV.2017.593"},{"key":"10.1016\/j.compag.2026.111852_b6","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J., 2019. Yolact: Real-time instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 9157\u20139166.","DOI":"10.1109\/ICCV.2019.00925"},{"key":"10.1016\/j.compag.2026.111852_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.aquaeng.2020.102117","article-title":"A modified YOLOv3 model for fish detection based on MobileNetv1 as backbone","volume":"91","author":"Cai","year":"2020","journal-title":"Aquac. Eng."},{"issue":"1","key":"10.1016\/j.compag.2026.111852_b8","doi-asserted-by":"crossref","DOI":"10.1111\/raq.13001","article-title":"Fish tracking, counting, and behaviour analysis in digital aquaculture: A comprehensive survey","volume":"17","author":"Cui","year":"2025","journal-title":"Rev. Aquac."},{"key":"10.1016\/j.compag.2026.111852_b9","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.-S., Lu, Q., 2019. Learning RoI transformer for oriented object detection in aerial images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2849\u20132858.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"10.1016\/j.compag.2026.111852_b10","series-title":"Biology and technology interaction: Study identifying the impact of robotic systems on fish behaviour change in industrial scale fish farms","author":"Evjemo","year":"2024"},{"key":"10.1016\/j.compag.2026.111852_b11","unstructured":"FAO, 2024. The State of World Fisheries and Aquaculture 2024 \u2013 Blue Transformation in Action. Rome."},{"key":"10.1016\/j.compag.2026.111852_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.aquaeng.2022.102244","article-title":"Fish feeding intensity quantification using machine vision and a lightweight 3D ResNet-GloRe network","volume":"98","author":"Feng","year":"2022","journal-title":"Aquac. Eng."},{"key":"10.1016\/j.compag.2026.111852_b13","article-title":"Precision fish farming: A new framework to improve production in aquaculture","author":"F\u00f8re","year":"2017","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2026.111852_b14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R., 2017. Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2961\u20132969.","DOI":"10.1109\/ICCV.2017.322"},{"key":"10.1016\/j.compag.2026.111852_b15","series-title":"CVAT: Computer vision annotation tool","author":"Intel Corporation","year":"2024"},{"key":"10.1016\/j.compag.2026.111852_b16","series-title":"Ultralytics YOLOv8","author":"Jocher","year":"2023"},{"key":"10.1016\/j.compag.2026.111852_b17","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al., 2023. Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"1\u20132","key":"10.1016\/j.compag.2026.111852_b18","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The hungarian method for the assignment problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"10.1016\/j.compag.2026.111852_b19","doi-asserted-by":"crossref","first-page":"201","DOI":"10.2307\/1540","article-title":"The length-weight relationship and seasonal cycle in gonad weight and condition in the perch (perca fluviatilis)","author":"Le Cren","year":"1951","journal-title":"J. Anim. Ecol."},{"key":"10.1016\/j.compag.2026.111852_b20","doi-asserted-by":"crossref","unstructured":"Li, P., Chen, X., Shen, S., 2019. Stereo R-CNN based 3D object detection for autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7644\u20137652.","DOI":"10.1109\/CVPR.2019.00783"},{"key":"10.1016\/j.compag.2026.111852_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107018","article-title":"CMFTNet: Multiple fish tracking based on counterpoised JointNet","volume":"198","author":"Li","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111852_b22","series-title":"OCEANS 2015-MTS\/IEEE Washington","first-page":"1","article-title":"Fast accurate fish detection and recognition of underwater images with Fast R-CNN","author":"Li","year":"2015"},{"key":"10.1016\/j.compag.2026.111852_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.aquaeng.2021.102222","article-title":"Recent advances in intelligent recognition methods for fish stress behavior","volume":"96","author":"Li","year":"2022","journal-title":"Aquac. Eng."},{"key":"10.1016\/j.compag.2026.111852_b24","series-title":"European Conference on Computer Vision","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"Liu","year":"2016"},{"key":"10.1016\/j.compag.2026.111852_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108099","article-title":"Where\u2019s your head at? Detecting the orientation and position of pigs with rotated bounding boxes","volume":"212","author":"Liu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111852_b26","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J., 2018. Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 8759\u20138768.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"10.1016\/j.compag.2026.111852_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108782","article-title":"ORP-Byte: A multi-object tracking method of pigs that combines oriented RepPoints and improved byte","volume":"219","author":"Lu","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"11","key":"10.1016\/j.compag.2026.111852_b28","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-oriented scene text detection via rotation proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.compag.2026.111852_b29","series-title":"Statistics for Engineers and Scientists","author":"Navidi","year":"2006"},{"key":"10.1016\/j.compag.2026.111852_b30","series-title":"2024 32nd Mediterranean Conference on Control and Automation","first-page":"567","article-title":"Framework for automated wound detection and tracking in industrial scale fish farms","author":"Nissen","year":"2024"},{"key":"10.1016\/j.compag.2026.111852_b31","doi-asserted-by":"crossref","unstructured":"Pedersen, M., Haurum, J.B., Bengtson, S.H., Moeslund, T.B., 2020. 3D-ZeF: A 3D zebrafish tracking benchmark dataset. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2426\u20132436.","DOI":"10.1109\/CVPR42600.2020.00250"},{"key":"10.1016\/j.compag.2026.111852_b32","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.1016\/j.compag.2026.111852_b33","series-title":"Yolov3: An incremental improvement","author":"Redmon","year":"2018"},{"issue":"6","key":"10.1016\/j.compag.2026.111852_b34","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compag.2026.111852_b35","series-title":"Sixteenth International Conference on Machine Vision","article-title":"StereoYolo+DeepSORT: A framework to track fish from underwater stereo camera in situ","volume":"Vol. 13072","author":"Saad","year":"2024"},{"issue":"4","key":"10.1016\/j.compag.2026.111852_b36","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1111\/raq.12143","article-title":"Application of machine vision systems in aquaculture with emphasis on fish: State-of-the-art and key issues","volume":"9","author":"Saberioon","year":"2017","journal-title":"Rev. Aquac."},{"issue":"8","key":"10.1016\/j.compag.2026.111852_b37","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"10.1016\/j.compag.2026.111852_b38","series-title":"Fish Physiology: Fish Biomechanics","author":"Shadwick","year":"2006"},{"key":"10.1016\/j.compag.2026.111852_b39","series-title":"SINTEF ACE","author":"SINTEF","year":"2023"},{"key":"10.1016\/j.compag.2026.111852_b40","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J., 2019. Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 5693\u20135703.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"10.1016\/j.compag.2026.111852_b41","series-title":"OCEANS 2017-Aberdeen","first-page":"1","article-title":"Vision based real-time fish detection using convolutional neural network","author":"Sung","year":"2017"},{"issue":"1","key":"10.1016\/j.compag.2026.111852_b42","doi-asserted-by":"crossref","first-page":"15642","DOI":"10.1038\/s41598-022-19932-9","article-title":"An affordable and easy-to-use tool for automatic fish length and weight estimation in mariculture","volume":"12","author":"Tonachella","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.111852_b43","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII 16","first-page":"649","article-title":"Solo: Segmenting objects by locations","author":"Wang","year":"2020"},{"key":"10.1016\/j.compag.2026.111852_b44","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H., 2020. CSPNet: A new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. pp. 390\u2013391.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"10.1016\/j.compag.2026.111852_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106512","article-title":"Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++","volume":"192","author":"Wang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111852_b46","series-title":"2017 IEEE International Conference on Image Processing","first-page":"3645","article-title":"Simple online and realtime tracking with a deep association metric","author":"Wojke","year":"2017"},{"key":"10.1016\/j.compag.2026.111852_b47","doi-asserted-by":"crossref","unstructured":"Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L., 2018. DOTA: A large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3974\u20133983.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"10.1016\/j.compag.2026.111852_b48","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., Wei, Y., 2018. Simple baselines for human pose estimation and tracking. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 466\u2013481.","DOI":"10.1007\/978-3-030-01231-1_29"},{"issue":"14","key":"10.1016\/j.compag.2026.111852_b49","doi-asserted-by":"crossref","first-page":"4142","DOI":"10.1049\/ipr2.12924","article-title":"Key point detection method for fish size measurement based on deep learning","volume":"17","author":"Yu","year":"2023","journal-title":"IET Image Process."},{"key":"10.1016\/j.compag.2026.111852_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.aquaculture.2023.740429","article-title":"Farmed Atlantic salmon (Salmo salar L.) avoid intrusive objects in cages: The influence of object shape, size and colour, and fish length","volume":"581","author":"Zhang","year":"2024","journal-title":"Aquaculture"},{"key":"10.1016\/j.compag.2026.111852_b51","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., Wang, X., 2022. ByteTrack: Multi-Object Tracking by Associating Every Detection Box. In: Proceedings of the European Conference on Computer Vision. ECCV.","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"10.1016\/j.compag.2026.111852_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106528","article-title":"EORNet: An improved rotating box detection model for counting juvenile fish under occlusion and overlap","volume":"124","author":"Zhang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.compag.2026.111852_b53","doi-asserted-by":"crossref","first-page":"4719","DOI":"10.1109\/TIP.2021.3074738","article-title":"Composited FishNet: Fish detection and species recognition from low-quality underwater videos","volume":"30","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.compag.2026.111852_b54","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.compag.2012.07.010","article-title":"The use of computer vision technologies in aquaculture\u2013A review","volume":"88","author":"Zion","year":"2012","journal-title":"Comput. Electron. Agric."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926004473?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926004473?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:23:24Z","timestamp":1783124604000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926004473"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":54,"alternative-id":["S0168169926004473"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111852","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FishKeypoints: A comprehensive stereo vision framework for fish behaviour monitoring and size measurement in aquaculture","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111852","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"111852"}}