Fabric wrinkle evaluation model with regularized extreme learning machine based on improved Harris Hawks optimization

Abstract

Aiming at the problem of low manual efficiency and inaccuracy in the traditional evaluation of fabric wrinkle-resistance performance relying on the eye of experts and subjective judgment, this paper proposes a fabric wrinkle grade evaluation model based on optimized regularized extreme learning machine. First, this paper extracts four image characteristics of fabric wrinkle image, including wrinkle density, gray level co-occurrence matrix, wavelet parameter standard deviation and data fusion feature, combined with the grade evaluation standard, and then obtains a complete wrinkle grade data set. Then, differential evolution is used to initialize the initial population of the Harris Hawks optimization, and the improved Harris Hawks optimization is used to optimize the input weights and hidden layer bias of the regularized extreme learning machine. Finally, this optimized regularized extreme learning machine is used to evaluate the fabric wrinkle grade. Experimental results show that the classification accuracy of the model proposed in this paper can reach 96.39%, and the proposed algorithm has no abnormal points in the analysis of the stability of the box plot.

KEYWORDS:

• Regularized extreme learning machine
• Harris Hawks optimization
• fabric wrinkle grade evaluation
• differential evolution

Additional information

Funding

This work is supported by National Key R&D Program of China (2017YFB1304000).