基于图像处理的机采棉杂质检测技术分析-analysis of impurity detection technology of machine-picked cotton based on image processing.docxVIP

AbstractThe yield of cotton in Xinjiang is very high, artificial picking is inefficient and high labor intensity, cotton machine picking is promoted quickly. But machine picked cotton has high trash content, that seriously impact the cotton’s production, processing and sale. Trash content has become the most important standard of cotton quality. Detection method of machine picked cotton trash existing is complex and inefficient. Image processing technology can achieve rapid detection of machine picked cotton impurities. This study proposed a detection technique of machine-picked cotton trash based on image processing to achieve rapid detection of cotton trash. The main content of the research and conclusion:Machine vision test bench was build, and designed the test light source. Accomplished camera communications, camera calibration and distortion testing.Light sources was selected and the illumination angle was determined. Determine the illumination area average illuminance with 6500lx. Accomplish camera communications with OPENCV, and camera calibration with MATLAB. Take the parameter of camera.object distance is 320mm, no vertical factor is 0. The camera distortion is less than 0.5 Pixel.Design and complete machine picked cotton trash detection. Take the parameter of material characteristic and analysis the difference of picking methods and primary cause. Separate trash artificially, classified the impurities, calculate weighing, counting, trash rate. According to the trash separation test data, the average rate of choose picked cotton trash is 13.40%, big trash occupy 16.9%, small trash occupy 83.1%. The average rate of all picked cotton trash is 23.47%, big trash occupy 47.8%, small trash occupy 52.2%.Using the color space conversion to machine pick cotton image segmentation to extract. Design of image processing impurities test, contrast RGB three-channel histogram to determine the background plate color;Analysis component gray scale contrast model, determin