Real-time fabric defect detection based on multi-scale convolutional neural network

Fabric defect detection plays an important role in ensuring quality control in the textile manufacturing industry. This study introduces a fabric defect detection method based on a multi-scale convolutional neural network (MSCNN) to improve accuracy and time efficiency. For detection accuracy, the MSCNN is constructed to obtain different scales of feature maps, which enhance the representation of tiny scale fabric defects. A faster defect locating method is designed with pre-known size information obtained by clustering analysis to reduce the computation time. An experiment is carried out for illustrating that the accuracy of MSCNN for each defect reaches over 92%, and the frames per second (FPS) is more than 29. Further analysis results demonstrate that the proposed MSCNN can accurately detect the fabric defects with a tiny scale, and the speed of detection can reach 30 m/min to satisfy the industrial requirements.