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FDMNet: A Multi-Task Network for Joint Detection and Segmentation of Three Fish Diseases
Zhuofu Liu1, Zigan Yan1, Gaohan Li1
1The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.
Journal of Imaging
|September 26, 2025
Summary
A new deep learning model, FDMNet, simultaneously detects and segments fish diseases. This multi-task approach improves accuracy and stability, offering practical solutions for aquaculture economic losses.
Area of Science:
- Aquaculture
- Computer Vision
- Deep Learning
Background:
- Fish diseases cause significant economic losses in aquaculture.
- Current deep learning models often have limitations in detecting single fish disease types or performing single tasks.
Purpose of the Study:
- To develop an advanced deep learning network, FDMNet, capable of simultaneous fish disease detection and lesion segmentation.
- To address the limitations of existing single-task models in aquaculture.
Main Methods:
- FDMNet is a multi-task learning network built on the YOLOv8 framework, incorporating a semantic segmentation branch with multi-scale perception.
- Utilizes the C2DF dynamic feature fusion module to prevent information loss during feature fusion across scales.
- Employs uncertainty-based loss weighting and PCGrad to manage conflicting gradients between detection and segmentation tasks.
Main Results:
- FDMNet achieved 97.0% mAP50 for detection and 85.7% mIoU for segmentation on a custom dataset of three fish diseases.
- Demonstrated a 2.5% improvement in detection mAP50 and a 5.4% improvement in segmentation mIoU compared to the YOLO-FD baseline.
- Showcased competitive accuracy in both detection and segmentation tasks.
Conclusions:
- FDMNet effectively performs simultaneous fish disease detection and segmentation.
- The proposed methods enhance model stability and performance, offering practical utility for aquaculture disease management.
