A Fast Monochromatic Fabric Defect Fast Detection Method Based on Convolutional Neural Network

Low efficiency,high false detection rate and high loss of manual fabric defect detection are problems lying in fabric manufacturing enterprises.Taking the problems as a starting point,this paper proposes a monochromatic fabric defect detection algorithm based on deep convolutional neural networks.Firstly,as the data scale of fabric defect is far smaller than that of large deep convolutional neural networks,it not only requires a large amount of calculation but is also likely to results in overfitting if large convolutional neural networks are adopted.Thus,we adopt shallow convolutional neural networks.Then,we propose a double network parallel model training method.The training process of using a large network to instruct a small network improves the training effect of the model.Finally,in an effort to make the deep convolutional neural network model release from GPU,to make it operate on computers,mobile and embedded devices at high speed,and to guarantee its detection accuracy,we propose a model compression algorithm combining optimization of convolution kernel parameters by feature maps.The results indicated that this fabric defect detection algorithm achieved high accuracy and high detection speed.In the mode of CPU on personal computers,its detection speed reaches 135 meters per minute and its accuracy reaches 96.99%.