Efficient detection method for foreign fibers in cotton

Since foreign fibers in cotton seriously affect the quality of the final cotton textile products,machine-vision-based detection systems for foreign fibers in cotton are receiving extensive attention in industrial equipment.As one of the key components in detection systems,the suitable and good classifier is significantly important for machine-vision-based on detection systems for foreign fibers in cotton due to it improving the system’s performance.In the study,we test five classifiers in the dataset of foreign fibers in cotton,and for finding the best feature set corresponding to the classifiers,we use the four filter feature selection approaches to find the best feature sets of foreign fibers in cotton corresponding to specific classifiers.The experimental results show that the extreme learning machine and kernel support vector machines have the excellent performance for foreign fiber detection and the classification accuracy are respectively 93.61%and 93.17%using the selected corresponding feature set with 42 and 52 features.

Fast processing of foreign fiber images by image blocking

In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton products.As the foundation of the foreign fiber automated inspection,image process exerts a critical impact on the process of foreign fiber identification.This paper presents a new approach for the fast processing of foreign fiber images.This approach includes five main steps,image block,image predecision,image background extraction,image enhancement and segmentation,and image connection.At first,the captured color images were transformed into gray-scale images;followed by the inversion of gray-scale of the transformed images;then the whole image was divided into several blocks.Thereafter,the subsequent step is to judge which image block contains the target foreign fiber image through image pre-decision.Then we segment the image block via OSTU which possibly contains target images after background eradication and image strengthening.Finally,we connect those relevant segmented image blocks to get an intact and clear foreign fiber target image.The experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation methods.On the other hand,this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.

Simulating the air flow in the conveying pipe of the foreign fiber detection device

Cotton bales are frequently intermingled with foreign fibers which will interfere in the process of spinning,weaving and dyeing and will worsen the product quality.Nowadays,cotton fibers are sorted manually in most of the cotton textile mills with very low efficiency.There is a great demand for foreign fiber detection devices in Chinese cotton textile mills.The air flow in the conveying pipe of the device has an important effect on the image acquisition,image analysis and removal of foreign fibers.As a primary effort,the air flow in the conveying pipe of the foreign fiber detection device was simulated numerically.The effects of the inlet air velocity on the air turbulence intensity and air velocity along the detecting section were studied.