Recent advances of target tracking applications in aquaculture with emphasis on fish

Mei, Yupeng; Sun, Boyang; Li, Bingbing; Yu, Huihui; Qin, Hanxiang; Liu, Huihui; Yan, Ning; Chen, Yingyi

doi:10.1016/j.compag.2022.107335

Cited by 25 publications

(7 citation statements)

References 128 publications

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“…The study highlights the innovations brought about by deep learning in visual tracking, particularly in understanding and managing fish behavior. This emerging field of research demonstrates the capabilities of deep learning to provide detailed insights into marine life, aiding in more scientific management and potentially reducing losses due to disease and other environmental factors [14].…”

Section: Related Workmentioning

confidence: 99%

Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries

Duong-Trung,

Duong-Trung

2024

EAI Endorsed Trans Ind Net Intel Syst

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This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.

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Section: Related Workmentioning

confidence: 99%

Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries

Duong-Trung,

Duong-Trung

2024

EAI Endorsed Trans Ind Net Intel Syst

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“…Underwater target detection technology is vital not only for military maritime defense tasks [ [1] , [2] , [3] , [4] ] but also for ecological environmental protection [ 5 , 6 ], and critical economic sectors, including fisheries and aquaculture [ [7] , [8] , [9] , [10] ]. This paper focuses on enhancing target detection performance in complex underwater environments.…”

Section: Introductionmentioning

confidence: 99%

MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Lian-suo,

Shen-hao,

Long-yu

2024

Heliyon

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“…In fish studies, animal movement information provides fundamental knowledge for understanding fish ecology. Advanced biological information is also important for the construction of fishery management plans (Crossin et al, 2017) and for reduc-ing disease and stress in individuals in aquaculture (Mei et al, 2022). Before technical advances in electronic tags were made, researchers collected information from direct observations at sea by fishing or from mark-recapture data by repeated measures on the same individuals (Hanselman et al, 2014;Lowerre-Barbieri et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Horizontal and vertical movement patterns of yellowtail (Seriola quinqueradiata) in the East Sea of Korea

Park,

Lee,

Baek

et al. 2024

Fish Aquat Sci

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The bio-logging method could be a valuable approach to studying the underwater movement of marine fish. We investigated the horizontal and vertical movement patterns of two yellowtails Seriola quinqueradiata weighing 8.7 kg and 9.5 kg with a popup satellite archival tag from October 2020 to January 2021 in the East Sea of Korea. Our results showed that a yellowtail migrated northward in October and November, and then shifted southward in mid-December. The average swimming depth and temperature of the fish monitored over 82 days were 24.9 ± 9.3 m (average ± SD) and 16.5 ± 1.9℃, respectively, and the total traveled distance was 1,172.4 km. The fish swam significantly deeper during the daytime (33.70 ± 14.80 m) than at nighttime (20.65 ± 8.44 m) from November to December (p < 0.05). These results suggest that the horizontal migratory route of yellowtails in accordance with the East Korea Warm Current which is the main branch of Tsushima Warm Current in the fall and early winter seasons, and showed significant diel vertical movement patterns from November to December.

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Recent advances of target tracking applications in aquaculture with emphasis on fish

Cited by 25 publications

References 128 publications

Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries

Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries

MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Horizontal and vertical movement patterns of yellowtail (Seriola quinqueradiata) in the East Sea of Korea

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