Clothing material classification-based deep learning

Feiqiang Fan; Diming Zhang

doi:10.1117/12.3031036

19 July 2024 Clothing material classification-based deep learning

Feiqiang Fan, Diming Zhang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131817J (2024) https://doi.org/10.1117/12.3031036
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Aiming at the problems of poor classification and recognition of traditional clothing images and low recognition accuracy, this paper proposes a convolutional neural network clothing material classification and recognition method based on the improved deep residual network (ResNet). By introducing an attention mechanism module in each residual block, a clothing material image classification and recognition model based on improved ResNet is constructed. In order to better carry out the related research, a self-constructed dataset specifically used for clothing material classification and recognition. In order to verify the superiority of the improved network in clothing material recognition, the traditional machine learning methods and the classical neural networks in deep learning are explored respectively, and the parameters of the network are adjusted to make it more suitable for clothing material recognition. The improved model is compared with other models. The experimental results show that the performance of the clothing material recognition network based on the improved ResNet network is superior (99.2% recognition accuracy, 98.46%precision, 98.83%recall, and 0.9864 F1_Score), and it can satisfy the needs of commercial networks for recognizing clothing materials

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Feiqiang Fan and Diming Zhang "Clothing material classification-based deep learning", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131817J (19 July 2024); https://doi.org/10.1117/12.3031036

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