Abstract
Classification of material type is crucial in the recycling industry since good quality recycling depends on the successful sorting of various materials. In textiles, the most commonly used fiber material types are wool, cotton, and polyester. When recycling fabrics, it is critical to identify and sort various fiber types quickly and correctly. The standard method of determining fabric fiber material type is the burn test followed by a microscopic examination. This traditional method is destructive, tedious, and slow since it involves cutting, burning, and examining the yarn of the fabric. We demonstrate that the identification procedure can be done nondestructively using optical coherence tomography (OCT) and deep learning. The OCT image scans of fabrics that are composed of different fiber material types such as wool, cotton, and polyester are used to train a deep neural network. We present the results of the created deep learning models’ capability to classify fabric fiber material types. We conclude that fiber material types can be identified nondestructively with high precision and recall by OCT imaging and deep learning. Because classification of material type can be performed by OCT and deep learning, this novel technique can be employed in recycling plants in sorting wool, cotton, and polyester fabrics automatically.
Highlights
To know the fiber content of a fabric is essential because fiber content directly determines the performance, usage, and care of the fabric [1,2,3]
Separate portions of the labeled optical coherence tomography (OCT) fabric scans are allocated to train and validate the deep learning architecture. e program Vision AutoML runs on cloud and works by using reinforcement learning (RL) and a recurrent neural network (RNN) that specifies the hyperparameters for a model
One can notice that the height of the OCT scan on the right is not optimized, resulting in the saturation in the image, which could have been a reason why the model did not provide the correct outcome for this specific input. is indicates that using high-quality OCT images for the deep learning training and the test plays a decisive role in the quality of the material classification
Summary
To know the fiber content of a fabric is essential because fiber content directly determines the performance, usage, and care of the fabric [1,2,3]. As a novel method, has been recently utilized in the automation and textile industry, especially in material [30] and fabric pattern recognition tasks [31,32,33]. We utilized the photonic imaging modality of OCT, combined with automated deep learning, in order to determine the fiber content of woven fabric. In this article, automated deep learning models were created that successfully identified fiber material types from OCT images. Ese scans were fed into a neural architecture search framework in order to accomplish fiber type identification from OCT images with automated deep learning.
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