计算机应用与软件2025，Vol.42Issue(6)：290-295,397,7.DOI:10.3969/j.issn.1000-386x.2025.06.038
基于改进YOLOv5的葡萄果穗检测算法
GRAPE BERRY DETECTION ALGORITHM BASED ON IMPROVED YOLOV5
摘要
Abstract
To address the problem of low detection accuracy due to the dense distribution and complex background of grape clusters in modern farms,a fast and accurate grape cluster detection method based on improved YOLOv5 is proposed.YOLOv5 was used as the base target detection model,and the feature extraction network was improved by using the coordinate attention mechanism to enhance its feature representation capability,and Bi-FPN was used for efficient fusion of image features to enhance the overall prediction capability of the network.The experimental results show that the detection accuracy of the model can reach 83.1％,which can effectively detect grape clusters in complex environments.关键词
葡萄/果穗检测/卷积神经网络/YOLOv5/目标检测Key words
Grapes/Clusters recognition/Convolutional neural network/YOLOv5/Target detection分类
计算机与自动化引用本文复制引用
吴子炜,徐达宇,夏芳,周素茵,潘青仙..基于改进YOLOv5的葡萄果穗检测算法[J].计算机应用与软件,2025,42(6):290-295,397,7.基金项目
国家自然科学基金项目(72001190) （72001190）
教育部人文社科基金项目(20YJC630173) （20YJC630173）
浙江省公益技术应用研究项目(GN22D010769) （GN22D010769）
浙江农林大学科研发展基金项目(2020FR060). （2020FR060）
