Defect detection of raised fabrics based on improved EfficientNet

Aiming at the problem that the surface of pile fabrics is soft and delicate, which is difficult to detect defects, this paper proposes a defect detection model for pile fabrics based on the improved EfficientNet. A deep neural network leveraging EfficientNet as its core feature extraction network is devised, incorporating the enhanced I-SPP spatial pyramid pooling module to enhance adaptability and accommodate input data across various scales. The SE module and Swish activation function in the MBConv module are replaced with CA attention module and Mish activation function to improve the model robustness and detection efficiency. Experiments show that the method proposed in this article improves the EfficientNet model achieves a mean average accuracy of 88.5% on the test set. Compared with other neural network architectures, this method improves the accuracy and efficiency of detection, which can meet the needs of related production enterprises.