Detection method of cohesive performance of raw silk based on machine vision

Aiming at the problem of poor precision of artificial detection of existing raw silk cohesiveness and no objective quantitative indicators, a method based on machine vision for detecting the cohesive performance of raw silk was proposed. Firstly, the collected raw silk images were subjected to binarization processing, interference information removal, image filling and raw silk edge detection, and single-pixel raw silk edge feature was obtained. Then, by calculating the linear distance between the upper and lower edge points of the raw silk, the relative change of the diameter of the raw silk was obtained, and the cracked area was determined according to the axial length of the change of the diameter of the raw silk. Finally, the cohesive performance of the raw silk was characterized by the times of raw silk cohesion frictions corresponding to the cracked area greater than 6 mm. The experimental results show that the diameter values of the 200 sets of raw silk measured by the detection method are compared with the diameter values measured by the microscope, and the errors are all within 5%, which satisfies the requirement of raw silk cohesion performance detection. Copyright No content may be reproduced or abridged without authorization.