Fish Disease Detection and Counting Using Enhanced YOLOv11 for Application in Aquatic Robots

Abstract

The rapidly growing global population creates urgent demand for protein and fish-based food sources. Consequently, the fisheries sector faces the dual challenge of meeting seafood supply needs while protecting marine ecosystems. AI and robotics-enabled smart aquaculture emerges as a critical solution to this dilemma. In high-density aquaculture systems, where disease transmission can escalate uncontrollably and cause catastrophic losses, computer vision integration has demonstrated exceptional efficacy through timely detection and enumeration of diseased fish. This paper analyzes a disease detection and counting system based on an enhanced YOLOv11 architecture. The model exhibits remarkable capability in identifying and localizing diseased specimens within complex underwater environments. On our manually annotated two-class dataset (diseased/healthy fish), it achieves 98.2% mAP@0.5, 76.9% mAP@0.5:0.95, 96.4% precision, and 95.2% recall. Critically, the system delivers not only real-time processing but also high accuracy, enabling prompt identification of piscine dermal pathologies for immediate intervention. This prevents widespread outbreaks and substantial economic damage. These findings pave the way for novel research trajectories and advanced intelligent solutions in future aquaculture development.