Abstract
The raising process has been widely used in manufacturing fabric productions. After raising the surface of the fabric, productions are covered with a fluff layer. The quality of the fabric surface is often valuated by the fluffing type. In order to objectively assess the fluff quality of the fabric surface, an optimal sensing method is proposed in this paper. The fluff contour image was firstly collected by the light-cut imaging device. Then, the fluff region was segmented by the adaptive image segmentation method, the contour coordinates of the fabric were extracted using the freeman chain code and constructed in the form of the binary image. Lastly, a back-propagation neural network (BPNN) was used to learn the relationship between the contour coordinates and the fluff quality. On this basis, a practical fabric fluff detection platform was developed based on the optimal sensing technique. Experimental tests were conducted to evaluate the performance of the proposed method in detecting the fluff quality with four different colours and different fluffing processes. Furthermore, the actual fabric inspection was carried out. The detection correct rate can reach 94.17 %, which can meet the practical production requirement.
Highlights
With the improvement of material quality, the quality requirement on the fabric surface is increasing
In order to meet the actual needs of the textile industry, this paper proposes an optimal sensing method based on back-propagation neural network (BPNN) to detect the quality of fluff fabrics
Because the contour coordinates of the upper edge of the images were extracted as the features, the proposed optimal sensing method is insensitive to the colour of the images
Summary
With the improvement of material quality, the quality requirement on the fabric surface is increasing. It is crucial to develop automatic detection method for the textile industry [3]. Chen and Feng [6] proposed a method based on two optimal filters for fabric defect detection, which can accurately detect the fabric defects, including colour images. Guan et al [9] applied the back-propagation neural network (BPNN) to achieve high-precision classification of pilling fabric images. In response to the actual needs of the textile industry, it is necessary to develop a practical method for detecting fluff quality of fabric surfaces. In order to meet the actual needs of the textile industry, this paper proposes an optimal sensing method based on BPNN to detect the quality of fluff fabrics. The raining of the BPNN was achieved by extracting the coordinate information of the upper edge of the fabric using the fluff-related images.
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