Defect Inspection System of Carbonized Bamboo Cane Based on LabView and Machine Vision

L. Yeni, Ye Shao-wei
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引用次数: 3

Abstract

A set of detection systems for online classification of carbonized bamboo cane based on virtual instrumentation and machine vision technology was developed pertinent to the problem that automation degree of defect detection of carbonized bamboo cane is low. Threshold segmentation and image filtering processing were conducted on images of bamboo cane with image processing technology, and then corresponding defect points were obtained through conducting morphological analysis and particle analysis. Rapid detection upgrading and date storage of carbonized bamboo cane were realized in this system. Experimental results indicate that classification of five kinds of bamboo canes with defects can be realized in this system and classification speed is 70mm/s; and average recognition rate of defect bamboo canes can reach above 90.6%.
基于LabView和机器视觉的炭化竹藤缺陷检测系统
针对炭化竹藤缺陷检测自动化程度不高的问题,开发了一套基于虚拟仪器和机器视觉技术的炭化竹藤在线分类检测系统。利用图像处理技术对竹藤图像进行阈值分割和图像滤波处理,然后通过形态学分析和颗粒分析得到相应的缺陷点。该系统实现了炭化竹藤的快速检测、升级和数据存储。实验结果表明,该系统可实现对5种缺陷竹条的分类,分类速度为70mm/s;竹藤缺陷的平均识别率可达90.6%以上。
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