江苏大学学报（自然科学版）2024，Vol.45Issue(1)：46-52,59,8.DOI:10.3969/j.issn.1671-7775.2024.01.007
一种轻量型果园环境果实检测方法
An environmental fruit detection method for light weight orchard
摘要
Abstract
Rapid and accurate detection of citrus in orchard environment is the key for autonomous picking robot.To solve the problems of excessive redundancy and imbalanced detection speed with accuracy of existing models,the light weight environmental fruit detection method was proposed.Based on YOLOv4,the Focal Loss was introduced to improve the negative sample mining ability of the model in binary classification detection task,and the optimized model pruning method was proposed to solve the problem of model parameter redundancy.The experimental results show that by the proposed method,the MAP of citrus fruit data set in orchard environment reaches 94.22%,which is improved by 1.18%compared with YOLOv4 model.The model parameters are reduced by 95.22%,and the model size is 4.84%of the original size with the detection speed increased by 4.03 times.关键词
柑橘/机器视觉/目标检测/焦点损失函数/模型剪枝Key words
citrus/machine vision/object detection/Focal Loss/model pruning分类
信息技术与安全科学引用本文复制引用
商高高,姜锟,韩江义,倪万磊..一种轻量型果园环境果实检测方法[J].江苏大学学报（自然科学版）,2024,45(1):46-52,59,8.基金项目
江苏省重点研发计划项目(BE2018343) （BE2018343）
园艺电动拖拉机研发项目(BE2017333) （BE2017333）
