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Fernandes MP, Costa AC, França HFDC, Souza AS, Viadanna PHDO, Lima LDC, Horn LD, Pierozan MB, de Rezende IR, de Medeiros RMDS, Braganholo BM, da Silva LOP, Nacife JM, de Pinho Costa KA, da Silva MAP, de Oliveira RF. Convolutional Neural Networks in the Inspection of Serrasalmids (Characiformes) Fingerlings. Animals (Basel) 2024; 14:606. [PMID: 38396574 PMCID: PMC10885909 DOI: 10.3390/ani14040606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics. To meet the growing demand, there is a need for automation and accuracy of processes, at a lower cost. Convolutional neural networks (CNNs) are a viable alternative for automation, reducing human intervention, work time, errors, and production costs. Therefore, the objective of this work is to evaluate the efficacy of convolutional neural networks (CNNs) in counting round fish fingerlings (Serrasalmidae) at different densities using 390 color photographs in an illuminated environment. The photographs were submitted to two convolutional neural networks for object detection: one model was adapted from a pre-trained CNN and the other was an online platform based on AutoML. The metrics used for performance evaluation were precision (P), recall (R), accuracy (A), and F1-Score. In conclusion, convolutional neural networks (CNNs) are effective tools for detecting and counting fish. The pre-trained CNN demonstrated outstanding performance in identifying fish fingerlings, achieving accuracy, precision, and recall rates of 99% or higher, regardless of fish density. On the other hand, the AutoML exhibited reduced accuracy and recall rates as the number of fish increased.
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Affiliation(s)
- Marília Parreira Fernandes
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Adriano Carvalho Costa
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Heyde Francielle do Carmo França
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Alene Santos Souza
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | | | - Lessandro do Carmo Lima
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Liege Dauny Horn
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Matheus Barp Pierozan
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Isabel Rodrigues de Rezende
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Rafaella Machado dos S. de Medeiros
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Bruno Moraes Braganholo
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Lucas Oliveira Pereira da Silva
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Jean Marc Nacife
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Kátia Aparecida de Pinho Costa
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Marco Antônio Pereira da Silva
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
| | - Rodrigo Fortunato de Oliveira
- Federal Institute of Education, Science and Technology of Goiás (IF Goiano)—Campus Rio Verde, Goiana South Highway, Km 01, Rio Verde 75901-970, GO, Brazil; (H.F.d.C.F.); (A.S.S.); (L.d.C.L.); (L.D.H.); (M.B.P.); (I.R.d.R.); (R.M.d.S.d.M.); (B.M.B.); (L.O.P.d.S.); (J.M.N.); (K.A.d.P.C.); (M.A.P.d.S.); (R.F.d.O.)
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Er MJ, Chen J, Zhang Y, Gao W. Research Challenges, Recent Advances, and Popular Datasets in Deep Learning-Based Underwater Marine Object Detection: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:1990. [PMID: 36850584 PMCID: PMC9966468 DOI: 10.3390/s23041990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been widely applied in practical applications, such as monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, etc. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness can be dramatically degraded when conventional approaches are used. Deep learning has been found to have significant impact on a variety of applications, including marine engineering. In this context, we offer a review of deep learning-based underwater marine object detection techniques. Underwater object detection can be performed by different sensors, such as acoustic sonar or optical cameras. In this paper, we focus on vision-based object detection due to several significant advantages. To facilitate a thorough understanding of this subject, we organize research challenges of vision-based underwater object detection into four categories: image quality degradation, small object detection, poor generalization, and real-time detection. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we provide a detailed critical examination of the most extensively used datasets. In addition, we present comparative studies with previous reviews, notably those approaches that leverage artificial intelligence, as well as future trends related to this hot topic.
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Abstract
The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise separable convolution into the network. Moreover, the training set used in the YOLO network is preprocessed by using a modified mosaic augmentation, in which the gray world algorithm is used to derive two images when performing mosaic augmentation. The recognition results and the comparison with the other target detectors demonstrate the effectiveness of the proposed YOLOv4 structure and the method of data preprocessing. According to both subjective and objective evaluation, the proposed target recognition strategy can effectively improve the accuracy and speed of underwater target recognition and reduce the requirement of hardware performance as well.
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