梳棉内层杂质高光谱图像检测

Detection of Trashes in Combed Cotton Using Hyper-spectral Images

以梳棉内部深度1～6 mm处的多种杂质为研究对象,研究了高光谱成像技术检测效果和可行性。在波长460～900 nm范围内,提取高光谱图像中杂质和棉花的像素光谱。采用二次判别分析分类像素,构造分割二值图像,使用面积过滤器和形态学组合方法剔除二值图像中的噪声点和伪目标,统计杂质检测效果。结果表明可以检测棉网内一定深度的普通杂质(植物性杂质)、彩色丙纶丝、有色线和有色布块,能够检测部分黑色毛发和灰色丙纶丝。其中,普通杂质的检测效果最佳,正确检测率超过80%。

This research focused on the detection of trashes at the depth of 1 - 6 mm in the cotton using hyper- spectral imaging and pixels classification. In the wavelength range of 460 -900 nm, the detection algorithms were developed based on pixel＇ s spectra of hyper-spectral image, pixel classification by quadratic discriminate analysis, and binary images post-processing by combining area filter with morphologic process. The results indicated that hyper-spectral imaging was able to detect some trashes at certain depths in the cotton, such as natural trashes, color polypropylene fibers, color yarn and fragments of cloth. And it also can detect some black hair and gray polypropylene fibers. In particular, the detection effect for natural trashes was the best. The detection rates for natural trashes were over 80%.