Fabric Defect Detection with Deep Learning
topic
Deep learning fabric inspection systems train convolutional neural network models on image datasets of 10000 to 100000 labelled defect images per defect type, achieving defect detection accuracy above 95 percent for common fabric faults including holes, stains, weaving errors, knitting defects, and finishing irregularities at production speeds above 100 metres per minute, with transfer learning enabling new defect type detection from smaller additional training datasets without full model retraining.
Role
Achieves detection accuracy and classification capability for complex fabric defects that rule-based image processing algorithms cannot match by learning the visual pattern characteristics of each defect type from example images rather than requiring explicit programming of defect features, enabling reliable detection of the full range of subtle and variable defect appearances encountered in industrial textile production across diverse fabric types and constructions.