Fabric surface characterization: assessment of deep learning-based texture representations using a challenging dataset

Abstract

Tactile sensing or fabric hand plays a critical role in an individual’s decision to buy a certain fabric from the range of available fabrics for a desired application. Therefore, textile and clothing manufacturers have long been in search of an objective method for assessing fabric hand, which can then be used to engineer fabrics with a desired hand. In this paper, we explore how to characterize surface properties (e.g. smoothness) of materials. We formulate the problem as a fine-grained texture classification problem, and study how deep learning-based texture representation techniques can help tackle the task. We introduce a new, challenging microscopic material surface dataset (CoMMonS), geared towards an automated fabric quality assessment mechanism in an intelligent manufacturing system. Additionally, we propose a multi-level texture encoding and representation network (MuLTER), which extracts texture details and structural information. Our dataset and source code are available at https://ghassanalregib.info/software-and-datasets.

Keywords:

• Texture representation and fine-grained texture classification
• material surface characterization
• fabric hand
• deep neural network
• texture dataset

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was partially funded by Kolon Industries, Inc. through the Kolon Center for Lifestyle Innovation at Georgia Tech.