渔业现代化2025，Vol.52Issue(1)：80-88,9.DOI:10.3969/j.issn.1007-9580.2025.01.008
基于改进YOLOv8n的水面残留饲料检测算法
A water surface residual feed detection algorithm based on improved YOLOv8n
摘要
Abstract
In aquaculture,the accumulation of leftover feed on the water surface not only leads to wastage but also contributes to deteriorating water quality,significantly impacting the well-being and growth of aquatic organisms.Conventional detection methods face challenges in accurately identifying small feed particles due to the intricate nature of aquatic environments.To tackle this issue,this research introduces an enhanced algorithm based on YOLOv8n for detecting residual feed on water surfaces.This algorithm improves the precision of detecting small feed particles by incorporating a specialized detection layer tailored for small targets.By amalgamating shallow and deep feature information,the algorithm enhances the network's ability to perceive targets across various scales,thereby boosting the accuracy of detecting small feed particles.Furthermore,the integration of the C2f_Faster_EMA module reduces model parameters,elevates detection speed,and fortifies the extraction of intricate features.Additionally,the devised ICBAM module bolsters the amalgamation of feature information for small targets,significantly enhancing detection accuracy.Experimental findings illustrate that the enhanced algorithm delivers exceptional performance across multiple evaluation metrics.Comparative to the original YOLOv8n,the mAP@0.5,precision,and recall rates have surged by 10.3%,7.6%,and 10.2%,respectively.Furthermore,the algorithm achieves a detection speed of 125 frames per second FPS,meeting the demands for real-time detection.These outcomes underscore the algorithm's capacity to swiftly and accurately identify residual feed on water surfaces,providing substantial technical backing for the intelligent administration of aquaculture.The implementation of this algorithm holds promise in efficiently curbing feed wastage,enhancing water quality,and amplifying the profitability of aquaculture operations.This advancement positions the aquaculture sector on a trajectory towards a more sustainable and efficient future.关键词
水面残留饲料/改进YOLOv8n/小目标检测层/C2f_Faster_EMA/ICBAMKey words
residual feed on water surface/improved YOLOv8n/small target detection layer/C2f_Faster_EMA/ICBAM分类
信息技术与安全科学引用本文复制引用
郑海锋,江林源,文露婷,周秀珊,介百飞,文家燕..基于改进YOLOv8n的水面残留饲料检测算法[J].渔业现代化,2025,52(1):80-88,9.基金项目
国家自然科学基金(61963006) （61963006）
广西自然科学基金面上项目(2018GXNSFAA050029,2018GXNSFAA294085) （2018GXNSFAA050029,2018GXNSFAA294085）
广西科技重大专项(桂科AA22068064,桂科AA22068066) （桂科AA22068064,桂科AA22068066）
广西重点研发计划(桂科AB23075093,桂科AB22035066) （桂科AB23075093,桂科AB22035066）
