摘要
随着化工产业自动化需求提升，基于机器视觉的化工原料自动分拣系统成为研究热点。通过改进图像识别算法，优化光源布置与机械结构设计，实现对化工原料颜色、形状、尺寸等特征的精准识别与高效分拣。经测试，系统分拣准确率超 98%，处理速度达每秒 5 件，有效提升化工原料分拣效率与精度，降低人工成本与操作风险，为化工行业智能化升级提供技术支撑。
关键词： 机器视觉；化工原料；自动分拣；图像识别；自动化控制
Abstract
With the increasing demand for automation in the chemical industry, an automated chemical raw material sorting system based on machine vision has become a research hotspot. By improving the image recognition algorithm, optimizing the light source layout and mechanical structure design, the system can accurately identify and efficiently sort the color, shape, size and other characteristics of chemical raw materials. After testing, the sorting accuracy of the system exceeds 98%, and the processing speed reaches 5 items per second, effectively improving the efficiency and precision of chemical raw material sorting, reducing labor costs and operational risks, and providing technical support for the intelligent upgrading of the chemical industry.
Key words： Machine vision; Chemical raw materials; Automatic sorting; Image recognition; Automation control
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