In order to solve the problem of poor detection effect of traditional image processing methods for the low contrast filament sizing defects in non-woven fabric production process， a low contrast filament sizing defect detection method of non-woven fabric based on Convolutional Neural Network （CNN） was proposed. Firstly， the collected non-woven fabric images were preprocessed to construct a defect dataset of filament sizing. Then， an improved convolutional neural network and a multi-scale feature sampling fusion module were used to construct an encoder to extract the semantic information of low contrast filament sizing 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 filament sizing was realized by training the network model on the constructed dataset. Experimental results show that， the proposed method can effectively locate and detect the low contrast filament sizing defects on non-woven fabric. The Mean Intersection over Union （MIoU） and category Mean Pixel Accuracy （MPA） of the proposed method can reach 77.32% and 86.17% respectively， and the average detection time of single sample of the proposed method is 50 ms， which can meet the requirements of industrial production.