{"refrec":{"BRefID":337222,"RR":"<b>Wang, G.; Van Stappen, G.; De Baets, B.</b> (2021). Automated detection and counting of <i>Artemia</i> using U-shaped fully convolutional networks and deep convolutional networks. <i>Exp. Syst. Appl. 171</i>: 114562. <a href=\"https://hdl.handle.net/10.1016/j.eswa.2021.114562\" target=\"_blank\">https://hdl.handle.net/10.1016/j.eswa.2021.114562</a>","BEntID":333844,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Exp. Syst. Appl. 171</i>: 114562. <a href=\"https://hdl.handle.net/10.1016/j.eswa.2021.114562\" target=\"_blank\">https://hdl.handle.net/10.1016/j.eswa.2021.114562</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Wang, G.; Van Stappen, G.; De Baets, B.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Wang, G.; Van Stappen, G.; De Baets, B.","Englishabstract":"The brine shrimp <em>Artemia</em> is a widely used cost-effective diet in aquaculture. In many <em>Artemia</em> studies, e.g., in a quality assessment of <em>Artemia</em> hatching, an automated method for detecting and counting the <em>Artemia</em> objects in images would be highly desired. However, there are few such works in literature. Moreover, it is very challenging to separate <em>Artemia</em> objects that are highly adjacent. In this paper, we propose a two-stage method for <em>Artemia</em> detection and counting, combining a target marker proposal network with a target classification network. In the first stage, the marker proposal network is implemented using U-shaped fully convolutional networks. This module can indicate target candidates, separate adjacent objects and obtain the object structural information simultaneously. In the second stage, using deep convolutional networks, we design a classifier to classify the target candidates into categories or label as a non-target, thereby obtaining the <em>Artemia</em> detection and counting results. Furthermore, an <em>Artemia</em> detection and counting dataset is collected to train and test the proposed method. Experimental results confirm that the proposed method can accurately detect and count the <em>Artemia</em> objects that have high degrees of adjacency in images, outperforming an <em>ad hoc</em> method based on hand-crafted features and the state-of-the-art YOLO-v3 method.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Automated detection and counting of <i>Artemia</i> using U-shaped fully convolutional networks and deep convolutional networks","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-17 01:32:38.697331","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"Object detection; Target classification; Artemia detection and counting; Marker proposal network; U-shaped fully convolutional network; Deep convolutional network","OtherDescriptors":null,"Notes":null,"AnaPub":2021,"MonPub":null,"DateUpdate":"2021-09-08","DateCreate":"2021-05-17","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000634863800003","VABBcode":null,"OpenAcc":0,"Handle":"10.1016/j.eswa.2021.114562"},"refs":null,"anarec":{"AnaID":337222,"PubliDate":2021,"Pagination":"114562","XtraPublOfAnaID":null,"ISBN":null,"Volume":"171","Issue":null,"BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":229019,"SerRR":"Expert Systems With Applications. 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