Automated disease identification in aquaculture utilizing underwater imaging and YOLOV10 network
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Abstract
In intense fish farming, continuous identification and surveillance of prevalent infectious diseases are crucial for formulating scientific methods for fish disease prevention, which may significantly reduce fish mortality and financial losses. Nonetheless, subpar underwater imagery and poorly identifiable targets pose significant obstacles to detecting infected fish. This research proposes an Automated Disease Identification (ADI) system using Underwater Imaging (UI) and an Improved YOLOV10 Network to address these problems. This work introduces an innovative residual awareness unit referred to as R-AU. This component is incorporated into the main structure of the YOLOv10 model to enhance the system's attention to the intricate biological features of targets during feature extraction. Using a bilateral feature triangle (BFT) with a dynamic combination of features in the head network augments the integration of contextual data from deeper layers. At the same time, localization signals from shallow layers improve the model's ability to differentiate objects from their surroundings. Studies conducted at a fishery platform indicated that the enhanced YOLOV10 framework outperformed the baseline YOLOV10, with mean accuracy increasing from 94.36% to 99.75%, reflecting an improvement of 5.39%. The enhanced YOLOV10 system can efficiently identify unhealthy fish and is suitable for large-scale aquaculture applications.
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