{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T11:21:38Z","timestamp":1772018498655,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16370-1","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T08:02:41Z","timestamp":1692259361000},"page":"24961-24981","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel detection model and platform for dead juvenile fish from the perspective of multi-task"],"prefix":"10.1007","volume":"83","author":[{"given":"Pan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jishu","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Lihong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hanwei","family":"Long","sequence":"additional","affiliation":[]},{"given":"Hongbo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Daoliang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"16370_CR1","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.procs.2020.07.023","volume":"175","author":"O Anas","year":"2020","unstructured":"Anas O, Wageeh Y, Mohamed HED et al (2020) Detecting abnormal fish behavior using motion trajectories in ubiquitous environments. Procedia Comput Sci 175:141\u2013148. https:\/\/doi.org\/10.1016\/j.procs.2020.07.023","journal-title":"Procedia Comput Sci"},{"key":"16370_CR2","doi-asserted-by":"publisher","unstructured":"Beyan C, Fisher RB (2013) Detecting abnormal fish trajectories using clustered and labeled data. 2013 IEEE Int Conf Image Process ICIP 2013 - Proc 1476\u20131480. https:\/\/doi.org\/10.1109\/ICIP.2013.6738303","DOI":"10.1109\/ICIP.2013.6738303"},{"key":"16370_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/sym11091179","volume":"11","author":"S Cheng","year":"2019","unstructured":"Cheng S, Zhao K, Zhang D (2019) Abnormal water quality monitoring based on visual sensing of three-dimensional motion behavior of fish. Symmetry (Basel) 11:1\u201320. https:\/\/doi.org\/10.3390\/sym11091179","journal-title":"Symmetry (Basel)"},{"key":"16370_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00227-019-3627-9","volume":"167","author":"ED Goldstein","year":"2020","unstructured":"Goldstein ED, Sponaugle S (2020) Juvenile reef fish growth and survival related to subregional patterns of primary production. Mar Biol 167:1\u201310. https:\/\/doi.org\/10.1007\/s00227-019-3627-9","journal-title":"Mar Biol"},{"key":"16370_CR5","doi-asserted-by":"publisher","first-page":"115051","DOI":"10.1016\/j.eswa.2021.115051","volume":"178","author":"J Hu","year":"2021","unstructured":"Hu J, Zhao D, Zhang Y et al (2021) Real-time nondestructive fish behavior detecting in mixed polyculture system using deep-learning and low-cost devices. Expert Syst Appl 178:115051. https:\/\/doi.org\/10.1016\/j.eswa.2021.115051","journal-title":"Expert Syst Appl"},{"key":"16370_CR6","doi-asserted-by":"publisher","unstructured":"Hu Z, Li XH, Xie XY, Zhao YC (2022) Abnormal Behavior Recognition of Underwater Fish Body Based on C3D Model. ACM Int Conf Proceeding Ser 92\u201397. https:\/\/doi.org\/10.1145\/3523150.3523165","DOI":"10.1145\/3523150.3523165"},{"key":"16370_CR7","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"16370_CR8","doi-asserted-by":"publisher","first-page":"101088","DOI":"10.1016\/j.ecoinf.2020.101088","volume":"57","author":"A Jalal","year":"2020","unstructured":"Jalal A, Salman A, Mian A et al (2020) Fish detection and species classification in underwater environments using deep learning with temporal information. Ecol Inform 57:101088. https:\/\/doi.org\/10.1016\/j.ecoinf.2020.101088","journal-title":"Ecol Inform"},{"key":"16370_CR9","doi-asserted-by":"publisher","unstructured":"Jie C, Yingying S, Junhui W, et al (2019) Intelligent Control and Management System for Recirculating Aquaculture. 2019 IEEE 2nd Int Conf Electron Commun Eng ICECE 2019 438\u2013443. https:\/\/doi.org\/10.1109\/ICECE48499.2019.9058567","DOI":"10.1109\/ICECE48499.2019.9058567"},{"key":"16370_CR10","doi-asserted-by":"publisher","unstructured":"Konovalov DA, Saleh A, Bradley M, et al (2019) Underwater Fish Detection with Weak Multi-Domain Supervision. Proc Int Jt Conf Neural Networks 2019-July:14\u201319. https:\/\/doi.org\/10.1109\/IJCNN.2019.8851907","DOI":"10.1109\/IJCNN.2019.8851907"},{"key":"16370_CR11","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1111\/jwas.12736","volume":"51","author":"D Li","year":"2020","unstructured":"Li D, Li C (2020) Intelligent aquaculture. J World Aquac Soc 51:808\u2013814. https:\/\/doi.org\/10.1111\/jwas.12736","journal-title":"J World Aquac Soc"},{"key":"16370_CR12","doi-asserted-by":"publisher","unstructured":"Li X, Shang M, Hao J, Yang Z (2016) Accelerating fish detection and recognition by sharing CNNs with objectness learning. Ocean 2016 - Shanghai 0\u20134. https:\/\/doi.org\/10.1109\/OCEANSAP.2016.7485476","DOI":"10.1109\/OCEANSAP.2016.7485476"},{"key":"16370_CR13","doi-asserted-by":"publisher","unstructured":"Li X, Shang M, Qin H, Chen L (2016) Fast accurate fish detection and recognition of underwater images with Fast R-CNN. Ocean 2015 - MTS\/IEEE Washingt 1\u20135. https:\/\/doi.org\/10.23919\/oceans.2015.7404464","DOI":"10.23919\/oceans.2015.7404464"},{"key":"16370_CR14","doi-asserted-by":"publisher","unstructured":"Li X, Tang Y, Gao T (2017) Deep but lightweight neural networks for fish detection. Ocean 2017 - Aberdeen 2017-Octob:1\u20135. https:\/\/doi.org\/10.1109\/OCEANSE.2017.8084961","DOI":"10.1109\/OCEANSE.2017.8084961"},{"key":"16370_CR15","doi-asserted-by":"publisher","first-page":"107435","DOI":"10.1016\/j.compag.2022.107435","volume":"203","author":"X Li","year":"2022","unstructured":"Li X, Hao Y, Zhang P et al (2022) A novel automatic detection method for abnormal behavior of single fish using image fusion. Comput Electron Agric 203:107435. https:\/\/doi.org\/10.1016\/j.compag.2022.107435","journal-title":"Comput Electron Agric"},{"key":"16370_CR16","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, et al (2016) SSD: Single shot multibox detector. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 9905 LNCS:21\u201337. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"16370_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-78181-w","volume":"10","author":"S Mati\u0107-Skoko","year":"2020","unstructured":"Mati\u0107-Skoko S, Vrdoljak D, Uvanovi\u0107 H et al (2020) Early evidence of a shift in juvenile fish communities in response to conditions in nursery areas. Sci Rep 10:1\u201316. https:\/\/doi.org\/10.1038\/s41598-020-78181-w","journal-title":"Sci Rep"},{"key":"16370_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-016-1138-y","volume":"17","author":"ZM Qian","year":"2016","unstructured":"Qian ZM, Wang SH, Cheng XE, Chen YQ (2016) An effective and robust method for tracking multiple fish in video image based on fish head detection. BMC Bioinforma 17:1\u201311. https:\/\/doi.org\/10.1186\/s12859-016-1138-y","journal-title":"BMC Bioinforma"},{"key":"16370_CR19","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. Comput Vis Pattern Recognit"},{"key":"16370_CR20","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ecoinf.2019.02.011","volume":"51","author":"A Salman","year":"2019","unstructured":"Salman A, Maqbool S, Khan AH et al (2019) Real-time fish detection in complex backgrounds using probabilistic background modelling. Ecol Inform 51:44\u201351. https:\/\/doi.org\/10.1016\/j.ecoinf.2019.02.011","journal-title":"Ecol Inform"},{"key":"16370_CR21","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1093\/icesjms\/fsz025","volume":"77","author":"A Salman","year":"2020","unstructured":"Salman A, Siddiqui SA, Shafait F et al (2020) Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system. ICES J Mar Sci 77:1295\u20131307. https:\/\/doi.org\/10.1093\/icesjms\/fsz025","journal-title":"ICES J Mar Sci"},{"key":"16370_CR22","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1093\/icesjms\/fsac166","volume":"79","author":"B Scoulding","year":"2022","unstructured":"Scoulding B, Maguire K, Orenstein EC (2022) Evaluating automated benthic fish detection under variable conditions. ICES J Mar Sci 79:2204\u20132216. https:\/\/doi.org\/10.1093\/icesjms\/fsac166","journal-title":"ICES J Mar Sci"},{"key":"16370_CR23","doi-asserted-by":"publisher","unstructured":"Sung M, Yu SC, Girdhar Y (2017) Vision based real-time fish detection using convolutional neural network. Ocean 2017 - Aberdeen 2017-Octob:1\u20136. https:\/\/doi.org\/10.1109\/OCEANSE.2017.8084889","DOI":"10.1109\/OCEANSE.2017.8084889"},{"key":"16370_CR24","unstructured":"Thida M, Eng HL, Chew BF (2009) Automatic analysis of fish behaviors and abnormality detection. Proc 11th IAPR Conf Mach Vis Appl MVA 2009 278\u2013282"},{"key":"16370_CR25","unstructured":"Wang C, Liao HM (2020) YOLOv4: Optimal Speed and Accuracy of Object Detection. Comput Vis Pattern Recognit"},{"key":"16370_CR26","doi-asserted-by":"publisher","unstructured":"Wang Q, Wu B, Zhu P, et al (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 11531\u201311539. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"16370_CR27","doi-asserted-by":"crossref","unstructured":"Wang C, Li Z, Wang T, et al (2021) Intelligent fish farm\u2014the future of aquaculture. Springer International Publishing","DOI":"10.1007\/s10499-021-00773-8"},{"key":"16370_CR28","doi-asserted-by":"publisher","first-page":"106512","DOI":"10.1016\/j.compag.2021.106512","volume":"192","author":"H Wang","year":"2022","unstructured":"Wang H, Zhang S, Zhao S et al (2022) Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++. Comput Electron Agric 192:106512. https:\/\/doi.org\/10.1016\/j.compag.2021.106512","journal-title":"Comput Electron Agric"},{"key":"16370_CR29","doi-asserted-by":"publisher","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM (2022) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Comput Vis Pattern Recognit 1\u201315. https:\/\/doi.org\/10.48550\/arXiv.2207.02696","DOI":"10.48550\/arXiv.2207.02696"},{"key":"16370_CR30","doi-asserted-by":"publisher","unstructured":"Xu W, Matzner S (2018) Underwater fish detection using deep learning for water power applications. Proc - 2018 Int Conf Comput Sci Comput Intell CSCI 2018 313\u2013318. https:\/\/doi.org\/10.1109\/CSCI46756.2018.00067","DOI":"10.1109\/CSCI46756.2018.00067"},{"key":"16370_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20164425","volume":"20","author":"W Xu","year":"2020","unstructured":"Xu W, Zhu Z, Ge F et al (2020) Analysis of behavior trajectory based on deep learning in ammonia environment for fish. Sensors (Switzerland) 20:1\u201311. https:\/\/doi.org\/10.3390\/s20164425","journal-title":"Sensors (Switzerland)"},{"key":"16370_CR32","doi-asserted-by":"publisher","unstructured":"Yang L, Liu Y, Yu H, et al (2021) Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: a review. Arch Comput Methods Eng 28:2785\u20132816. https:\/\/doi.org\/10.1007\/s11831-020-09486-2","DOI":"10.1007\/s11831-020-09486-2"},{"key":"16370_CR33","doi-asserted-by":"publisher","unstructured":"Yu G, Wang L, Hou M et al (2020) An adaptive dead fish detection approach using SSD-MobileNet. Proc - 2020 Chinese Autom Congr CAC, pp 1973\u20131979. https:\/\/doi.org\/10.1109\/CAC51589.2020.9326648","DOI":"10.1109\/CAC51589.2020.9326648"},{"key":"16370_CR34","doi-asserted-by":"publisher","first-page":"106714","DOI":"10.1016\/j.compag.2022.106714","volume":"193","author":"P Zhang","year":"2022","unstructured":"Zhang P, Li D (2022) EPSA-YOLO-V5s: A novel method for detecting the survival rate of rapeseed in a plant factory based on multiple guarantee mechanisms. Comput Electron Agric 193:106714. https:\/\/doi.org\/10.1016\/j.compag.2022.106714","journal-title":"Comput Electron Agric"},{"key":"16370_CR35","doi-asserted-by":"publisher","first-page":"107098","DOI":"10.1016\/j.compag.2022.107098","volume":"198","author":"S Zhao","year":"2022","unstructured":"Zhao S, Zhang S, Lu J et al (2022) A lightweight dead fish detection method based on deformable convolution and YOLOV4. Comput Electron Agric 198:107098\u2013107109. https:\/\/doi.org\/10.1016\/j.compag.2022.107098","journal-title":"Comput Electron Agric"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16370-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16370-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16370-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T13:46:25Z","timestamp":1708609585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16370-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,17]]},"references-count":35,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16370"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16370-1","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,17]]},"assertion":[{"value":"25 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}