Fabric defect detection method

Abstract

The present disclosure provides a fabric defect detection method, including the following steps: constructing a data set; preprocessing the data set; constructing a region-based convolutional neural network (R-CNN) model for fabric defect detection; where the R-CNN model for fabric defect detection includes four convolutional layers, four max-pooling layers, and two fully connected layers; training the R-CNN model for fabric defect detection; and reducing a number of false negative (FN) samples by classification threshold reduction. The present disclosure provides a novel R-CNN model for fabric defect detection. The model provides a desirable feature detection accuracy, has a low running cost, and is easy to implement, such that the model can be better applicable to actual operations in an industrial environment.

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