Defect detection of raised fabrics based on improved EfficientNet

Chang Xu; Shoufeng Jin; Jie Zan

doi:10.1117/12.3050436

24 October 2024 Defect detection of raised fabrics based on improved EfficientNet

Chang Xu, Shoufeng Jin, Jie Zan

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960V (2024) https://doi.org/10.1117/12.3050436
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

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.

Citation Download Citation

Chang Xu, Shoufeng Jin, and Jie Zan "Defect detection of raised fabrics based on improved EfficientNet", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960V (24 October 2024); https://doi.org/10.1117/12.3050436

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KEYWORDS

Defect detection

Data modeling

3D modeling

Neural networks

Visual process modeling

Ablation

Performance modeling

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