Fabric Defect Detection Method Based on Automatic Machine Learning
While solving many practical problems,the methods based on convolutional neural network(CNN)have shown better performance than traditional machine learning methods.However,as to a specific problem and data set,an efficient CNN model can only be designed based on knowing domain knowledge completely.Usually it will take plenty of computing resource and time to finish the designing procedure.This paper proposes a new automatic machine learning method(named rdpsoCNN)for fabric defect detection,the random drift particle swarm optimization algorithm(RDPSO)is used to automatically find an optimal CNN structure of one-class deep support vector data description.The experimental results show that the proposed rdpsoCNN can find good CNN structures that achieve better detection performance compared to the state-of-art designs.
automatic machine learningconvolutional neural networkone-class deep support vector data descriptionran-dom drift particle swarm optimizationdefect detection
万方数据
维普
