摘要
设计并实现了一种高效的智能物料分拣系统,集成了图像采集技术、图像处理算法以及深度学习中的残差网络识别技术,构建了一个精准智能的自动化平台。系统利用工业相机实时捕获物料信息,通过预处理优化识别效果。借助OpenCV进行图像特征提取,预训练的ResNet模型则确保了对各类物料的高精度识别。识别结果传递给PLC,由其驱动分拣机构完成自动化分类。此外,系统还配备了实时数据反馈机制,监控分拣流程,确保系统运行的连续性与稳定性。
This article designs and implements an efficient intelligent material sorting system that integrates image acquisition technology,image processing algorithms,and residual network recognition technology in deep learning,constructing a precise and intelligent automation platform.The system uses industrial cameras to capture material information in real time,and optimizes the recognition effect through pre-processing.With OpenCV for image feature extraction,the pre-trained ResNet model ensures high-precision identification of various materials.The recognition results are passed to the PLC,which drives the sorting mechanism to complete the automatic classification.In addition,the system is equipped with a real-time data feedback mechanism to monitor the sorting process and ensure the continuity and stability of the system operation.
作者
张柱
刘海龙
ZHANG Zhu;LIU Hailong(Hunan Railway Professional Technology College,Hunan Zhuzhou 412000,China)
出处
《工业仪表与自动化装置》
2025年第3期20-23,共4页
Industrial Instrumentation & Automation
基金
湖南省教育厅科学研究项目“基于改进MeanShift算法的全景视觉目标跟踪研究”(23C0816)。
