Research and design of a machine vision-based silk cocoon quality inspection system

Abstract

Silk cocoon is one of the critical textile raw materials, and its quality has a significant impact on production and processing. In view of the problems such as time-consuming, labor-intensive, and low efficiency in the existing silk cocoon quality inspection methods, this paper proposes a machine vision-based silk cocoon quality inspection system. For different types of silk cocoons, multiple machine vision techniques are used for image processing and feature extraction. The quality characteristics of silk cocoons are discriminated and analyzed by machine learning algorithms to achieve automatic detection of the cocoon quality. Experimental results show that the proposed system has high accuracy and fast detection speed and can meet the requirements of automated detection in the silk cocoon production process.