圣女果表面缺陷检测与分级系统研究

Surface Defect Detection and Classification System for Cherry Tomatoes

设计了一种基于机器视觉的圣女果表面缺陷检测与大小分级系统。该系统集图像采集、图像处理与识别及机器人分拣于一体,实现了对图像进行滤波、二值化、边缘检测和定位识别;提出了基于Sobel与分数阶微分的边缘检测算法,通过比较7种不同算法对同一批圣女果分拣的效果,检验新算法的有效性。实验结果表明:所提算法可以有效地检测圣女果缺陷并对圣女果进行大小分级,综合分级准确率为98.4%,具有很好的应用前景。

A system with functions of surface defect detection and classification for cherry tomatoes based on machine vision was proposed.The system integrated image acquisition,image processing,image recognition and robotic palletizing,which could achieve image filtering,binarization,edge detection and location identification.An edge-detection algorithm was proposed based on fractional differential and Sobel operator.The effectiveness of the proposed algorithm was tested by comparing the sorting results of seven different algorithms on the same parcel of cherry tomatoes.Experimental results showed that the algorithm could effectively detect defects and classify the cherry tomatoes.The comprehensive classification accuracy was 98.4% with a good application prospect.