关键词: 无纺布, 低对比度, 浆丝缺陷, 语义分割, 深度学习

Abstract: In order to solve the problems of low contrast of pulp filament defects in non-woven fabric production process and poor detection effect of traditional image processing methods, a new low contrast pulp filament defect detection method of non-woven fabrics based on convolutional neural network was proposed. Firstly, the collected non-woven fabric images were preprocessed to construct the defect data set of pulp filament. Then, an improved convolutional neural network and multi-scale feature sampling fusion module were used to construct an encoder to extract the semantic information of low contrast of pulp filament defects, and a skip connection was used in the decoder to achieve multi-scale feature fusion for optimizing the upsampling module. Finally, the low contrast defect detection of pulp filament was realized by training the network model of the constructed data set. The experimental results show that, the proposed method can effectively locate and detect the low contrast pulp filament defects on non-woven fabrics. The mean intersection over union and category mean pixel accuracy of the proposed method can reach 77.32% and 86.17%, and the average detection time of single sample is 50 ms, which can meet the requirements of industrial production.

Key words: non-woven fabric, low contrast, pulp filament defect, semantic segmentation, deep learning