农业机械学报2025，Vol.56Issue(10)：94-101,139,9.DOI:10.6041/j.issn.1000-1298.2025.10.009
多目标鱼类实例分割LIS-YOLO轻量化模型研究
Lightweight LIS-YOLO Model for Multi-target Fish Instance Segmentation
摘要
Abstract
Accurate segmentation of underwater objects laid the foundation for studying aspects such as the behaviour and biomass of aquatic animals.However,existing underwater instance segmentation algorithms often lacked sufficient robustness when facing underwater environment-specific interferences,such as suspended particles,colour attenuation,and background noise,and enhancing the generalization ability of lightweight models in unstructured and dynamic underwater scenes remained a key challenge.To this end,a lightweight instance segmentation lightweight instance segmentation YOLO v8(LIS-YOLO)model was proposed,which can effectively segment sturgeon,juvenile bass,and adult bass from different shooting angles,and a real-time underwater object segmentation system was developed based on PyQt5.Firstly,a lightweight and high-precision C2f-Faster-EMA module was designed,in which the original complex C2f module was replaced with a more lightweight C2f-Faster module,and an efficient multi-scale attention mechanism was integrated to improve the feature extraction capability for small-object fish.Secondly,Wise-IoU was introduced into the improved model to reduce harmful gradients caused by low-quality samples,thereby enhancing the model's segmentation capability in complex environments.Finally,a real-time multi-object instance segmentation system for underwater objects was developed by using a graphical user interface and the PyQt5 framework,enabling the visualization of different fish species.The experimental results showed that the LIS-YOLO model achieved precision,mean average precision,floating-point operations,and frame rate of 97.2％,95.9％,3.60 × 1010,and 127 f/s,respectively.The number of model parameters was compressed to 9.0 × 106,accounting for 76.3％of the original model.This research result not only provided an accurate and lightweight instance segmentation model for underwater object recognition but also explored the effectiveness of fish segmentation from different shooting angles,offering practical application value for improving the level of intelligent aquaculture.关键词
深度学习/实例分割/多目标鱼类/YOLO v8/PyQt5/轻量化模型Key words
deep learning/instance segmentation/multi-object fish/YOLO v8/PyQt5/lightweight model分类
信息技术与安全科学引用本文复制引用
徐文凯,郎平,李道亮..多目标鱼类实例分割LIS-YOLO轻量化模型研究[J].农业机械学报,2025,56(10):94-101,139,9.基金项目
国家自然科学基金项目(32373186) （32373186）
