基于机器视觉的辣椒外部品质检测

Study on external quality detection of pepper based on machine vision

为实现正常辣椒和缺陷辣椒(黑斑、虫蚀和花皮)的快速检测,通过图像采集,利用中值滤波去噪;提取样本图像24个颜色特征值,20个纹理特征值,利用Otsu算法阈值分割,提取2个形态特征值;选用连续投影法(Successive Projections Algorithm,SPA)优选14个特征值,结合最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)方法进行建模,SPA-LS-SVM模型检测结果为98.67%,模型最优。

To realize the rapid detection of normal peppers and defective peppers(dark spots,insect marks and stripe skin),image acquisition and median filtering were used for denoising.24 color feature variables and 20 texture feature variables of sample images were extracted.Threshold segmentation in the Otsu algorithm was used to extract two morphological feature variables.The Successive Projections Algorithm(SPA) was used to optimize 14 feature variables.Modeling was done based on the Least Squares Support Vector Machine(LS-SVM)method.The detection result of SPA-LS-SVM model is 98.67%,which is the best.