{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:55:36Z","timestamp":1769849736895,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T00:00:00Z","timestamp":1686009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T00:00:00Z","timestamp":1686009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2019203195"],"award-info":[{"award-number":["F2019203195"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106214"],"award-info":[{"award-number":["62106214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11042-023-15465-z","type":"journal-article","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T06:02:09Z","timestamp":1686031329000},"page":"7097-7117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["JF-YOLO: the jellyfish bloom detector based on deep learning"],"prefix":"10.1007","volume":"83","author":[{"given":"Wengming","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Feng","family":"Rui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5348-3147","authenticated-orcid":false,"given":"Cunjun","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Haibin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yaqian","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,6]]},"reference":[{"key":"15465_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17705\/3jmwa.000065","volume":"2021","author":"LS Ambati","year":"2021","unstructured":"Ambati LS, El-Gayar O (2021) Human activity recognition: a comparison of machine learning approaches. J Midwest Assoc Inform Syst (JMWAIS) 2021:1. https:\/\/doi.org\/10.17705\/3jmwa.000065","journal-title":"J Midwest Assoc Inform Syst (JMWAIS)"},{"issue":"6","key":"15465_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11852-021-00845-0","volume":"25","author":"L An","year":"2021","unstructured":"An L, Wang L, Ou D, Jia C, Li W, Ding Y, You C, Liao J, Huang H (2021) The ecological mechanisms of Acetes blooms as a threat to the security of cooling systems in coastal nuclear power plants. J Coast Conserv 25 (6):1\u201310","journal-title":"J Coast Conserv"},{"issue":"1","key":"15465_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13717-020-00268-z","volume":"9","author":"SK Baliarsingh","year":"2020","unstructured":"Baliarsingh S K, Lotliker A A, Srichandan S, Samanta A, Kumar N, Nair TM B (2020) A review of jellyfish aggregations, focusing on India\u2019s coastal waters. Ecol Process 9(1):1\u20139","journal-title":"Ecol Process"},{"key":"15465_CR4","doi-asserted-by":"crossref","unstructured":"Barrado C, Fuentes J A, Salam\u00ed E, Royo P, Olariaga A D, L\u00f3pez J, Fuentes V L, Gili J M, Pastor E (2014) Jellyfish monitoring on coastlines using remote piloted aircraft. In: IOP conference series: Earth and environmental science, vol 17. IOP Publishing, p 012195","DOI":"10.1088\/1755-1315\/17\/1\/012195"},{"key":"15465_CR5","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-Y M (2020) Yolov4: optimal speed and accuracy of object detection. arXiv:2004.10934"},{"issue":"2","key":"15465_CR6","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1080\/23308249.2020.1806201","volume":"29","author":"M Bosch-Belmar","year":"2020","unstructured":"Bosch-Belmar M, Milisenda G, Basso L, Doyle T K, Leone A, Piraino S (2020) Jellyfish impacts on marine aquaculture and fisheries. Rev Fisheries Sci Aquacult 29(2):242\u2013259","journal-title":"Rev Fisheries Sci Aquacult"},{"key":"15465_CR7","doi-asserted-by":"crossref","unstructured":"Chen Q, Wang Y, Yang T, Zhang X, Cheng J, Sun J (2021) You only look one-level feature. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13039\u201313048","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"15465_CR8","doi-asserted-by":"crossref","unstructured":"El-Gayar O F, Ambati L S, Nawar N (2020) Wearables, artificial intelligence, and the future of healthcare. IGI Global. https:\/\/www.igi-global.com\/chapter\/wearables-artificial-intelligence-and-the-future-of-healthcare\/www.igi-global.com\/chapter\/wearables-artificial-intelligence-and-the-future-of-healthcare\/236337","DOI":"10.4018\/978-1-5225-9687-5.ch005"},{"key":"15465_CR9","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.ecoser.2014.12.004","volume":"11","author":"A Ghermandi","year":"2015","unstructured":"Ghermandi A, Galil B, Gowdy J, Nunes PALD (2015) Jellyfish outbreak impacts on recreation in the Mediterranean Sea: Welfare estimates from a socioeconomic pilot survey in Israel. Ecosyst Serv 11 :140\u2013147","journal-title":"Ecosyst Serv"},{"key":"15465_CR10","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"15465_CR11","doi-asserted-by":"crossref","unstructured":"Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: European conference on information retrieval. Springer, pp 345\u2013359","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"15465_CR12","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3354\/meps254129","volume":"254","author":"WM Graham","year":"2003","unstructured":"Graham W M, Martin D L, Martin J C (2003) In situ quantification and analysis of large jellyfish using a novel video profiler. Mar Ecol Prog Ser 254:129\u2013140. https:\/\/doi.org\/10.3354\/meps254129","journal-title":"Mar Ecol Prog Ser"},{"issue":"14","key":"15465_CR13","doi-asserted-by":"publisher","first-page":"19429","DOI":"10.1007\/s11042-021-11307-y","volume":"81","author":"Y Han","year":"2022","unstructured":"Han Y, Chang Q, Ding S, Gao M, Zhang B, Li S (2022) Research on multiple jellyfish classification and detection based on deep learning. Multimed Tools Applic 81(14):19429\u201319444. https:\/\/doi.org\/10.1007\/s11042-021-11307-y","journal-title":"Multimed Tools Applic"},{"key":"15465_CR14","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"15465_CR15","doi-asserted-by":"publisher","unstructured":"Influence of the digital divide and socio-economic factors on prevalence of diabetes. Issues In Information Systems (2020) https:\/\/doi.org\/10.48009\/4_iis_2020_103-113","DOI":"10.48009\/4_iis_2020_103-113"},{"issue":"6","key":"15465_CR16","doi-asserted-by":"publisher","first-page":"8389","DOI":"10.3233\/JIFS-189157","volume":"39","author":"A Kamili","year":"2020","unstructured":"Kamili A, Fatima I, Hassan M, Parah S A, Vijaya Kumar V, Ambati L S (2020) Embedding information reversibly in medical images for e-health. J Intell Fuzzy Syst 39(6):8389\u20138398. https:\/\/doi.org\/10.3233\/JIFS-189157","journal-title":"J Intell Fuzzy Syst"},{"key":"15465_CR17","doi-asserted-by":"publisher","unstructured":"Kim D, Shin J-, Kim H, Lee D, Lee S-M, Myung H (2013) Experimental tests of autonomous jellyfish removal robot system JEROS. In: Kim J-H, Matson E T, Myung H, Xu P (eds) Robot intelligence technology and applications 2012, vol 208. Springer, Berlin, pp 395\u2013403. https:\/\/doi.org\/10.1007\/978-3-642-37374-9_38","DOI":"10.1007\/978-3-642-37374-9_38"},{"key":"15465_CR18","doi-asserted-by":"crossref","unstructured":"Kim D, Kim H, Jung S, Koo J, Kim J, Myung H (2015) A vision-based detection algorithm for moving jellyfish in underwater environment. In: 2015 12th International conference on ubiquitous robots and ambient intelligence (URAI). IEEE, pp 144\u2013145","DOI":"10.1109\/URAI.2015.7358846"},{"key":"15465_CR19","doi-asserted-by":"publisher","unstructured":"Kim H, Kim D, Jung S, Koo J, Shin J-U, Myung H (2015) Development of a UAV-type jellyfish monitoring system using deep learning. In: 2015 12th International conference on ubiquitous robots and ambient intelligence (URAI). IEEE, Goyang, pp 495\u2013497. https:\/\/doi.org\/10.1109\/URAI.2015.7358813","DOI":"10.1109\/URAI.2015.7358813"},{"issue":"8","key":"15465_CR20","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1109\/JSEN.2016.2517823","volume":"16","author":"H Kim","year":"2016","unstructured":"Kim H, Koo J, Kim D, Jung S, Shin J-U, Lee S, Myung H (2016) Image-based monitoring of jellyfish using deep learning architecture. IEEE Sens J 16(8):2215\u20132216. https:\/\/doi.org\/10.1109\/JSEN.2016.2517823","journal-title":"IEEE Sens J"},{"key":"15465_CR21","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.ecoinf.2019.05.004","volume":"52","author":"AB Labao","year":"2019","unstructured":"Labao A B, Naval P C Jr (2019) Cascaded deep network systems with linked ensemble components for underwater fish detection in the wild. Eco Inform 52:103\u2013121","journal-title":"Eco Inform"},{"issue":"7553","key":"15465_CR22","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"21","key":"15465_CR23","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ifacol.2018.09.412","volume":"51","author":"J Li","year":"2018","unstructured":"Li J, Su Z, Geng J, Yin Y (2018) Real-time detection of steel strip surface defects based on improved yolo detection network. IFAC-PapersOnLine 51(21):76\u201381","journal-title":"IFAC-PapersOnLine"},{"key":"15465_CR24","doi-asserted-by":"crossref","unstructured":"Liang T, Chu X, Liu Y, Wang Y, Tang Z, Chu W, Chen J, Ling H (2021) Cbnetv2: a composite backbone network architecture for object detection. arXiv:2107.00420","DOI":"10.1109\/TIP.2022.3216771"},{"key":"15465_CR25","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"15465_CR26","doi-asserted-by":"publisher","first-page":"24784","DOI":"10.1109\/ACCESS.2020.2971253","volume":"8","author":"J Liu","year":"2020","unstructured":"Liu J, Yu C, Hu Z, Zhao Y, Bai Y, Xie M, Luo J (2020) Accurate prediction scheme of water quality in smart mariculture with deep Bi-S-SRU learning network. IEEE Access: Practical Innovations, Open Solutions 8:24784\u201324798","journal-title":"IEEE Access: Practical Innovations, Open Solutions"},{"key":"15465_CR27","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg A C (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"15465_CR28","doi-asserted-by":"crossref","unstructured":"Liu Y, Wang Y, Wang S, Liang T, Zhao Q, Tang Z, Ling H (2020) Cbnet: a novel composite backbone network architecture for object detection. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 11653\u201311660","DOI":"10.1609\/aaai.v34i07.6834"},{"issue":"6","key":"15465_CR29","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.3390\/s20061708","volume":"20","author":"M Martin-Abadal","year":"2020","unstructured":"Martin-Abadal M, Ruiz-Frau A, Hinz H, Gonzalez-Cid Y (2020) Jellytoring: real-time jellyfish monitoring based on deep learning object detection. Sensors 20(6):1708. https:\/\/doi.org\/10.3390\/s20061708","journal-title":"Sensors"},{"key":"15465_CR30","doi-asserted-by":"publisher","first-page":"102279","DOI":"10.1016\/j.jag.2020.102279","volume":"97","author":"B Mcilwaine","year":"2021","unstructured":"Mcilwaine B, Rivas Casado M (2021) JellyNet: the convolutional neural network jellyfish bloom detector. Int J Appl Earth Obs Geoinf 97:102279. https:\/\/doi.org\/10.1016\/j.jag.2020.102279","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"15465_CR31","unstructured":"Misra D (2019) Mish: a self regularized non-monotonic activation function. arXiv:1908.08681"},{"issue":"2","key":"15465_CR32","first-page":"113","volume":"34","author":"Y Miyao","year":"2014","unstructured":"Miyao Y, Isobe A, et al. (2014) An application of low-altitude remote sensing using a vessel-towed balloon for monitoring jellyfish patchiness in coastal waters. J Remote Sens Soc Jpn 34(2):113\u2013120","journal-title":"J Remote Sens Soc Jpn"},{"key":"15465_CR33","doi-asserted-by":"crossref","unstructured":"Peng F, Miao Z, Li F, Li Z (2021) S-FPN: a shortcut feature pyramid network for sea cucumber detection in underwater images. Expert Syst Appl, 115306","DOI":"10.1016\/j.eswa.2021.115306"},{"issue":"2","key":"15465_CR34","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1002\/zoo.20218","volume":"28","author":"J Pierce","year":"2009","unstructured":"Pierce J (2009) Prediction, location, collection and transport of jellyfish (Cnidaria) and their polyps. Zoo Biol 28(2):163\u2013176. https:\/\/doi.org\/10.1002\/zoo.20218","journal-title":"Zoo Biol"},{"key":"15465_CR35","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv:1804.02767"},{"key":"15465_CR36","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inform Process Syst 28:91\u201399","journal-title":"Adv Neural Inform Process Syst"},{"issue":"4","key":"15465_CR37","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1109\/JOE.2003.819315","volume":"28","author":"J Rife","year":"2003","unstructured":"Rife J, Rock SM (2003) Segmentation methods for visual tracking of deep-ocean jellyfish using a conventional camera. IEEE J Oceanic Eng 28(4):595\u2013608. https:\/\/doi.org\/10.1109\/JOE.2003.819315","journal-title":"IEEE J Oceanic Eng"},{"issue":"7","key":"15465_CR38","doi-asserted-by":"publisher","first-page":"2343","DOI":"10.1007\/s10530-009-9648-4","volume":"12","author":"A Roohi","year":"2010","unstructured":"Roohi A, Kideys A E, Sajjadi A, Hashemian A, Pourgholam R, Fazli H, Khanari A G, Eker-Develi E (2010) Changes in biodiversity of phytoplankton, zooplankton, fishes and macrobenthos in the Southern Caspian Sea after the invasion of the ctenophore Mnemiopsis leidyi. Biol Invasions 12(7):2343\u20132361","journal-title":"Biol Invasions"},{"key":"15465_CR39","first-page":"0","volume":"10","author":"SS Samsuri","year":"2017","unstructured":"Samsuri SS, Arshad MR, AManaf A, Yaacob MIH (2017) Detection of jellyfish using acoustic sensor. Methods (San Diego, Calif.) 10:0\u20130049","journal-title":"Methods (San Diego, Calif.)"},{"key":"15465_CR40","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","volume":"157","author":"Y Tian","year":"2019","unstructured":"Tian Y, Yang G, Wang Z, Wang H, Li E, Liang Z (2019) Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Comput Electron Agricul 157:417\u2013426","journal-title":"Comput Electron Agricul"},{"key":"15465_CR41","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 workshops, pp 390\u2013391","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"15465_CR42","doi-asserted-by":"crossref","unstructured":"Wang K, Liew J H, Zou Y, Zhou D, Feng J (2019) Panet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9197\u20139206","DOI":"10.1109\/ICCV.2019.00929"},{"key":"15465_CR43","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon I S (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"15465_CR44","doi-asserted-by":"publisher","unstructured":"Xiufen Wang, Huiyuan Wang, Song Wang (2011) Jellyfish detection based on K-FOE residual map and ring segmentation. In: 2011 IEEE 13th international conference on communication technology. IEEE, Jinan, pp 762\u2013766. https:\/\/doi.org\/10.1109\/ICCT.2011.6157979","DOI":"10.1109\/ICCT.2011.6157979"},{"key":"15465_CR45","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ijleo.2019.02.038","volume":"183","author":"Z Yi","year":"2019","unstructured":"Yi Z, Yongliang S, Jun Z (2019) An improved tiny-yolov3 pedestrian detection algorithm. Optik 183 :17\u201323","journal-title":"Optik"},{"key":"15465_CR46","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhang X (2019) Automatic recognition and counting method of deep-sea jellyfish based on image multi-feature matching. In: 2019 11th International conference on intelligent human-machine systems and cybernetics (IHMSC), vol 1. IEEE, pp 233\u2013236","DOI":"10.1109\/IHMSC.2019.00061"},{"key":"15465_CR47","doi-asserted-by":"crossref","unstructured":"Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2020) Distance-IoU loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 12993\u201313000","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"15465_CR48","doi-asserted-by":"crossref","unstructured":"Zi W, Yong T, Yuanyuan F, Wei M, Shuai W, Xiaolin L (2020) Monitoring of biomass at cooling water system of hongyanhe nuclear power plant by using acoustic methods. In: E3S web of conferences, vol 194. EDP Sciences, p 01007","DOI":"10.1051\/e3sconf\/202019401007"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15465-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15465-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15465-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T07:07:24Z","timestamp":1704697644000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15465-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,6]]},"references-count":48,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["15465"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15465-z","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,6]]},"assertion":[{"value":"14 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 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":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}