Real-time detection and identification of fish skin health in the underwater environment based on improved YOLOv10 model

Real-time detection and identification of fish skin health in the underwater environment based on improved YOLOv10 model

In densely populated aquaculture net cages, real-time detection and identification of fish skin diseases can effectively prevent large-scale outbreaks, thereby reducing fish mortality rates and economic losses. This study proposes an identification model, DCW-YOLO, based on deep learning-driven obje...

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Bibliographic Details
Main Authors: Duanrui Wang, Meng Wu, Xingyue Zhu, Qiwei Qin, Shaowen Wang, Haibin Ye, Kaiyuan Guo, Chi Wu, Yi Shi
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Aquaculture Reports
Subjects:
Fish disease
Improved YOLOv10 network
Aquaculture
Real-time
Miichthys miiuy
Online Access:http://www.sciencedirect.com/science/article/pii/S2352513425001097
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