Work

Advancing Circular Economy Practices Using AI-Powered Colour Classification of Textile Fabrics: Overview and Roadmap

Year: 2025

Type: article

Abstract: Classification is a crucial task for reintroducing end-of-life fabrics as raw materials in a circular process, thus reducing reliance on dyeing processes. In this context, this review explores the evolution of automated and semi-automated colour classification methods, emphasizing the transition from deterministic techniques to advanced methods, with a focus on machine learning, deep learning, and particularly Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). These technologies show potential for improving accuracy and efficiency. The results highlight the need for enriched datasets, deeper AI integration into industrial processes, and alignment with circular economy objectives to enhance sustainability without compromising industrial performance. Tested against a case study, the different architectures proved to be effective in classification with better performance reached by CNN-based methods, which consistently outperforms other methods in most colour families, with an average accuracy of 86.1%, indicating its robustness and adaptability for this task.

Source: Textiles

DOI: 10.3390/textiles5040053

Author: Rocco Furferi

Institution: University of Florence

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,207