Fabric defect classification method based on improved extreme learning machine

The invention relates to a fabric defect classification method based on an improved extreme learning machine. The method comprises the steps that a defect image of a training sample is preprocessed; adaptive wavelets are constructed for decomposition to detect fabric defects, and feature extraction is conducted through a multi-feature fusion method to obtain defect features; the defect features are classified, and in the classifying process, an online ELM algorithm is introduced, and online ELM pruning is conducted on hidden nodes through a sensitivity analysis method. By the adoption of the method, the deficiency of the overall processing mode of bulk data through an ELM is overcome, dependency of algorithm performance on the hidden nodes is reduced, and pruning of the hidden nodes is conducted based on sensitivity analysis.