Fabric surface defect detection method based on multi-scale feature fusion neural network
Fabric surface defect detection is a crucial step in the fabric production process.In recent years,machine vision methods have been increasingly proposed for fabric defect detection.However,these methods have several limitations that affect their accuracy and the detection rate of unknown defects.This paper proposed a fabric surface defect detection method that utilizes a multi-scale feature fusion neural network,leveraging the encoder-decoderarchitecture.The feature extraction module of the encoder utilizes various convolution kernels and dilated convolutions to detect defects of varying sizes.Additionally,it combines residuals and incorporates a feature enhancement and filtering module to optimize the utilization of multi-scale information,enhancing the association between dif-ferent features.Finally,a loss function is designed that combines weighted cross-entropy and Dice Loss to facilitate model convergence.Experiments show that this method can effectively segment out the defect parts of the fabric surface,achieving an accuracy rate of 98.96％and a recall rate of 96.95％on the AITEX dataset.
万方数据
维普
