A novel method for identification of cotton contaminants based on machine vision

Foreign matter is easily mixed into cotton during picking, storing, drying, transporting, purchasing, and processing. These contaminants are difficult to remove in the spinning process and can cause yarn breakage, thus reducing efficiency of working. This paper proposed the new method based on machine vision to measure the contaminants in raw cottons. The color images of cottons with contaminants are acquired and divided three channels images. Intensity of illumination of cottons often is unstable because of the driving voltage of light source unsteady. The intensity of illumination of images should be corrected for measuring correction and precision. The Gamma adjustment function was adopted to correct non-uniform illumination for images. Through the experimental contrast, Gamma correction parameter is set as 0.8. The Otsu method is used to segment the image. After images of three channels’ information fusing, the contaminants of cotton samples can be correctly detected and cotton seeds also can be effectively inspected. The false detection ratio of the measuring system is less than 5%. The experimental results show the measuring system can meet with the requirement of the cotton's industry application.