Automatic Assessment of Fabric Smoothness Appearance Based on a Compact Convolutional Neural Network With Label Smoothing

• Main Authors: Jingan Wang, Kangjun Shi, Lei Wang, Zhengxin Li, Fengxin Sun, Ruru Pan, Weidong Gao
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Fabric smoothness; textile testing; convolutional neural network; label smoothing
• Online Access: https://ieeexplore.ieee.org/document/8981949/

In the textile and apparel industry, it remains a challenging task to evaluate the fabric smoothness objectively. In existing studies, the objective fabric smoothness assessment is defined as a typical image classification problem. However, the fabric smoothness labels contain sequence information, and the problem shall be defined as an ordinal classification problem. This article presents an effective method including an image preprocessing algorithm, a compact convolutional neural network(CNN) model, and a label smoothing process. Compared with the commonly used CNN frameworks, the proposed compact CNN model is more suitable for this small-sample and low-abstraction problem. The image processing algorithm can improve the model's illumination adaptability, and the label smoothing process can modify the model to satisfy the ordinal classification problems better. In the experiments, the method is tested on a fabric image set including 385 graded fabric specimens. Within a 10-fold cross validation, the proposed method achieves 84.00%, 95.38%, and 100% average accuracies under errors of 0 degree, 0.5 degree, and 1 degree respectively. Implementation discussions on preprocessing and label smoothing verify their effectiveness in improving model performance in assessment accuracies and illumination stability. The proposed method outperforms the state-of-the-art methods for fabric smoothness assessment and a series of widely used deep learning methods. Promisingly, the proposed method can provide novel research ideas for the image-based fabric smoothness assessment.