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Automated detection and counting of Artemia using U-shaped fully convolutional networks and deep convolutional networks

Gang WANG, Gilbert Van Stappen (UGent) and Bernard De Baets (UGent)
(2021) EXPERT SYSTEMS WITH APPLICATIONS. 171.
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Abstract
The brine shrimp Artemia is a widely used cost-effective diet in aquaculture. In many Artemia studies, e.g., in a quality assessment of Artemia hatching, an automated method for detecting and counting the Artemia objects in images would be highly desired. However, there are few such works in literature. Moreover, it is very challenging to separate Artemia objects that are highly adjacent. In this paper, we propose a two-stage method for Artemia 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 Artemia detection and counting results. Furthermore, an Artemia 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 Artemia objects that have high degrees of adjacency in images, outperforming an ad hoc method based on hand-crafted features and the state-of-the-art YOLO-v3 method.
Keywords
General Engineering, Artificial Intelligence, Computer Science Applications, Object detection, Target classification, Artemia detection and counting, Marker proposal network, U-shaped fully convolutional network, Deep convolutional network

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MLA
WANG, Gang, et al. “Automated Detection and Counting of Artemia Using U-Shaped Fully Convolutional Networks and Deep Convolutional Networks.” EXPERT SYSTEMS WITH APPLICATIONS, vol. 171, 2021, doi:10.1016/j.eswa.2021.114562.
APA
WANG, G., Van Stappen, G., & De Baets, B. (2021). Automated detection and counting of Artemia using U-shaped fully convolutional networks and deep convolutional networks. EXPERT SYSTEMS WITH APPLICATIONS, 171. https://doi.org/10.1016/j.eswa.2021.114562
Chicago author-date
WANG, Gang, Gilbert Van Stappen, and Bernard De Baets. 2021. “Automated Detection and Counting of Artemia Using U-Shaped Fully Convolutional Networks and Deep Convolutional Networks.” EXPERT SYSTEMS WITH APPLICATIONS 171. https://doi.org/10.1016/j.eswa.2021.114562.
Chicago author-date (all authors)
WANG, Gang, Gilbert Van Stappen, and Bernard De Baets. 2021. “Automated Detection and Counting of Artemia Using U-Shaped Fully Convolutional Networks and Deep Convolutional Networks.” EXPERT SYSTEMS WITH APPLICATIONS 171. doi:10.1016/j.eswa.2021.114562.
Vancouver
1.
WANG G, Van Stappen G, De Baets B. Automated detection and counting of Artemia using U-shaped fully convolutional networks and deep convolutional networks. EXPERT SYSTEMS WITH APPLICATIONS. 2021;171.
IEEE
[1]
G. WANG, G. Van Stappen, and B. De Baets, “Automated detection and counting of Artemia using U-shaped fully convolutional networks and deep convolutional networks,” EXPERT SYSTEMS WITH APPLICATIONS, vol. 171, 2021.
@article{8691223,
  abstract     = {{The brine shrimp Artemia is a widely used cost-effective diet in aquaculture. In many Artemia studies, e.g., in a quality assessment of Artemia hatching, an automated method for detecting and counting the Artemia objects in images would be highly desired. However, there are few such works in literature. Moreover, it is very challenging to separate Artemia objects that are highly adjacent. In this paper, we propose a two-stage method for Artemia 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 Artemia detection and counting results. Furthermore, an Artemia 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 Artemia objects that have high degrees of adjacency in images, outperforming an ad hoc method based on hand-crafted features and the state-of-the-art YOLO-v3 method.}},
  articleno    = {{114562}},
  author       = {{WANG, Gang and Van Stappen, Gilbert and De Baets, Bernard}},
  issn         = {{0957-4174}},
  journal      = {{EXPERT SYSTEMS WITH APPLICATIONS}},
  keywords     = {{General Engineering,Artificial Intelligence,Computer Science Applications,Object detection,Target classification,Artemia detection and counting,Marker proposal network,U-shaped fully convolutional network,Deep convolutional network}},
  language     = {{eng}},
  pages        = {{13}},
  title        = {{Automated detection and counting of Artemia using U-shaped fully convolutional networks and deep convolutional networks}},
  url          = {{http://doi.org/10.1016/j.eswa.2021.114562}},
  volume       = {{171}},
  year         = {{2021}},
}
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