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

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%.