Density detection of knitted fabrics based on improved ResNet18

At present, traditional image processing methods for identifying the density of knitted fabrics with different organizational structures face problems such as poor adaptability and low accuracy. A method based on an improved deep residual neural network model is proposed to detect the density of knitted fabrics. Firstly, a collection system for fabric images was established, and a fabric image dataset was constructed; Secondly, all convolutional layers with a stride of 2 are replaced with SPD convolution to reduce the loss of fine-grained information. A Dilation wise Residual (DWR) module is added to the residual layer to enhance the network's ability to capture local and global features; Finally, the SmoothLoss function was introduced to improve the accuracy of detection while accelerating convergence. The experimental results show that the method has an error of less than 2% in detecting the density of various fabrics, and has stronger adaptability to different types.