基于机器视觉的苹果表损智能检测系统设计
Design of apple damage automatic detection system based on machine vision
秦寅初 1李涛 1李旭 2王美玲 3谭治英2
作者信息
- 1. 常州大学机械与轨道交通学院,江苏常州 213164
- 2. 河海大学机电工程学院,江苏常州 213200
- 3. 中国科学院合肥物质科学研究所智能机械研究所,安徽合肥 230031
- 折叠
摘要
[目的]满足苹果外观品质和大小综合分级的现实需求,解决中国苹果人工分选效率低,分选设备结构复杂、成本高等问题.[方法]提出一种YOLOv5s-apple模型,在主干网络中引入Transformer模块和CBAM注意力模块,同时加入加权双向特征金字塔网络(Bi-FPN)改进颈部网络,并结合HALCON软件,利用自行设计的一种苹果表损智能检测系统进行表损分拣和大小分级.[结果]与原YOLOv5s模型相比,YOLOv5s-apple模型的mAP提升了 6.2％,检测系统的分拣分级准确率可达97.5％,试验系统的处理速度为5 s/个.[结论]试验系统可以有效地进行苹果分级分选.
Abstract
[Objective]To meet the practical requirements for comprehensive grading based on the appearance quality and size of apples,and to address issues such as low efficiency of manual sorting,complex structure,and high cost of sorting equipment for Chinese apples.[Methods]A YOLOv5s-apple model was proposed.The transformer module and CBAM attention module were introduced into the backbone network,and the weighted Bidirectional feature pyramid network(Bi-FPN)was added to improve the neck network.Then,combined with HALCON software,a self-designed intelligent apple damage detection system was used to carry out damage sorting and size classification.[Results]The experimental results showed that compared with the original YOLOv5s model,the mAP of the YOLOv5s-Apple model was improved by 6.2％,and the accuracy of apple sorting system could reach 97.5％,the processing speed of the system was 5 s/apple.[Conclusion]The system can effectively carry out apple grading and sorting,and provide a reference for the intellectualization and low cost of Apple detection equipment.
关键词
苹果/分选/无损检测/深度学习/YOLOv5/注意力机制Key words
apple/sorting/non-destructive testing/deep learning/YOLOv5/attention mechanisms引用本文复制引用
基金项目
江苏省产业前瞻与关键核心技术重点项目(BE2021016-4)
出版年
2024
国家科技期刊平台
NSTL
万方数据