Fabric wrinkle rating model based on ResNet18 and optimized random vector functional-link network

Abstract

To improve the accuracy and efficiency of the objective evaluation of the fabric wrinkle model, the R18-COTSA-RVFL model is proposed in this paper. The fabric wrinkle rating model is based on the combination of ResNet18 (R18) and the enhanced random vector functional-link network, in which the enhanced random vector functional-link network replaces the Softmax layer of ResNet18. This model uses ResNet18 (R18) to extract features from wrinkled images. In addition, a random vector functional-link network model improved by the tunicate swarm algorithm was proposed for wrinkle-level classification. First, we used chaotic-logistic maps and opposition-based learning to optimize the initial population of the tunicate swarm algorithm. The proposed search agent fragment mapping method enables the tunicate swarm algorithm simultaneously to optimize the hidden layer node parameters and bias, input layer weight, and activation function of the random vector functional-link network, thus avoiding the inaccurate classification result caused by random parameter selection in the random vector functional-link network. From comparative experiments, the proposed R18-COTSA-RVFL has the highest average classification accuracy and stability.