基于改进Yolov8的羽绒羽毛识别与分类方法  

Recognition and Classification of Duck Down Based on Improved YOLOv8

作　　者：姜阔胜 张道同 周远远 任杰[1] JIANG Kuosheng;ZHANG Daotong;ZHOU Yuanyuan;REN Jie(School of Mechanical Engineering,Anhui University of Science and Technology,Huainan 232001,China;State Key Laboratory for Manufacturing System Engineering,School of Mechanical Engineering,Xi′an Jiaotong University,Xi′an 710049,China)

机构地区：[1]安徽理工大学机电工程学院,安徽淮南232001 [2]西安交通大学机械制造系统工程国家重点实验室,陕西西安710049

出　　处：《洛阳理工学院学报(自然科学版)》2025年第1期74-80,共7页Journal of Luoyang Institute of Science and Technology:Natural Science Edition

基　　金：国家重点研发计划项目(2020YFB1314203,2020YFB1314103);江西省教育厅科技研究项目(GJJ210639);教育部供需对接就业教育项目(20220100621).

摘　　要：鉴于羽绒羽毛成分检测人工方法效率低、主观因素大的问题,提出一种基于改进YOLOv8的羽绒羽毛识别方法,通过图像预处理实现图像增强,减少复杂背景影响。通过改进YOLOv8算法实现了羽绒羽毛成分的精准识别,采用轻量化卷积GSconv增加模型检测速度,提高对多目标检测能力。加入CBAM模块,提高特征提取能力和检测效率,提高模型对小目标检测能力。改进的YOLOv8算法模型对羽绒羽毛成分识别的平均准确率达到99.1%。Due to the inefficient and subjective identification of the duck down,a duck down recognition method based on the improved YOLOv8 algorithm is proposed,which realizes image enhancement through image preprocessing and reduces the influence of complex background.The improved YOLOv8 algorithm realized the accurate identification of duck down compositions.The lightweight modules GSconv and VoV-GSCSP can speed up model detection and improve the ability to detect multiple targets.Adding the CBAM module can improve the feature extraction ability and detection accuracy,and improve its ability to detect small targets.The average accuracy of the improved YOLOv8 algorithm for the identification of duck down is 99.1%.

关 键 词：羽绒羽毛 改进YOLOv8 小目标 多目标 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]