The obtainment and recognition of raw silk defects based on machine vision and image analysis

摘要

At present, the raw silk defects detection is traditional seriplane inspection that is greatly influenced by human factors and poorly repeatable. We introduce a method of machine vision and image analysis to intuitively detect the main kinds of raw silk defects which are loops and loose end in this paper. During the experiment, we develop an image acquisition system that includes a charge coupled device line scan sensor, a telecentric lens, a light source, and a raw silk winding device to capture the raw silk images continuously and steadily. After the image capture stage, the defect segmentation using thresholding and morphology operations, such as opening, hole fill up, and image subtraction, is carried out to extract the geometrical features accurately. To describe the defects under visual property, four geometrical features are extracted, which will be used as the input of BP neural network. A BP neural network is designed as a classifier to recognize the test samples. Experimented results indicate that the proposed method can successfully detect and recognize the main kinds of raw silk defects.