基于机器视觉的内层包装缺陷检测光源的优化

目的 针对食品内层包装纸的缺陷特征，对机器视觉识别系统的照明光源进行优化，以提高内层包装缺陷的识别率，减少缺陷包装量。方法 基于计算机视觉识别技术，通过斑点检测不同光源下内层包装纸的常见缺陷特征，分别采用Matlab的三维绘图、相关性分析的方法，依次确定照明光源类型、形状和角度，并进行应用验证。结果 红外光源为纸铝复合内层包装纸缺陷特征识别的最适光源类型；条形光源与内层包装纸呈极显著相关，缺陷识别率达96.95%；60°的高角度照明位置与内层包装纸呈显著相关，缺陷识别率达96.96%。红外条形光源高角度照明，缺陷识别率达99%。结论 将红外条形光源高角度照明应用于纸铝复合内层包装纸的在线检测，与LED环形光源相比，其缺陷特征视觉识别率提高了0.51个百分点。

Optimization of Light Source for Defect Detection of Inner Packaging Paper Based on Machine Vision

The work aims to optimize the light source of the machine vision recognition system based on the defect features of the food inner packaging, in order to improve the recognition rate of the inner packaging defects and reduce the amount of defective packaging. Based on computer vision recognition technology, the common defect features of inner packaging paper under different light sources were detected by spots. The type, shape and angle of light source were determined in order respectively by means of Matlab three-dimensional drawing and correlation analysis for application verification. The result was that, the infrared light source was the most suitable light source type for the defect feature recognition of paper aluminum composite inner packaging paper. The strip light source was extremely significantly correlated with the inner packaging paper, with the defect recognition rate of 96.95%.The 60° high angle illumination position was significantly correlated with the inner packaging paper, with the defect recognition rate of 96.96%. For the infrared strip light source under high-angle illumination, the defect recognition rate was 99%. Compared with LED ring light source, the infrared strip light source under high-angle illumination is applied to the on-line detection of paper aluminum composite inner packaging paper and its visual recognition rate of defect features is increased by 0.51 percentage points.