PLDNet: real-time Plectropomus leopardus disease recognition

Abstract

In Plectropomus leopardus, Vibrio disease and Hirudo parasitic disease are relatively common. Timely recognition of these diseases can improve the survival rate of Plectropomus leopardus and prevent their spread. However, early-stage diseases are difficult to distinguish due to their small size and subtle characteristics. Traditional manual recognition methods rely on personal experience and subjective judgment, leading to time-consuming and error-prone diagnoses. To address the challenges in detecting and classifying Plectropomus leopardus diseases, this paper proposes PLDNet (Plectropomus Leopardus Disease Detection Network), a real-time detection and recognition method that provides faster and more accurate diagnoses for fish farms. PLDNet incorporates two significant advancements: First, it employs FocalModulation, which enhances the model’s ability to identify key disease characteristics in images. Second, it introduces the MPDIoU (Minimum Point Distance-based Intersection over Union) for bounding box similarity comparison, optimizing the loss function and improving recognition accuracy. This paper also presents the PLDD (Plectropomus Leopardus Disease Dataset), a newly developed dataset that includes comprehensive images of healthy and diseased specimens. PLDD addresses the scarcity of data for this species and serves as a valuable resource for advancing research in marine fish health. Empirical validation of PLDNet was conducted using the PLDD dataset and benchmarked against leading models, including YOLOv8-n, YOLOv9-m, and YOLOv9-c. The results show that PLDNet achieves superior detection performance, with an average detection accuracy of 84.5%, a recall rate of 86.6%, an mAP@o.5 of 88.1%, and a real-time inference speed of 45 FPS. These metrics demonstrate that PLDNet significantly outperforms other models in both accuracy and efficiency, providing practical solutions for real-time fish disease management.