Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models

TL;DR

This paper explores using RGB images and advanced deep learning models, including transfer learning and zero-shot foundation models, to improve automated textile sorting and recycling processes.

Contribution

It introduces a novel combination of transfer learning and foundation models for textile classification and segmentation using RGB imagery.

Findings

EfficientNetB0 achieved 81.25% accuracy in textile classification.

Zero-shot segmentation with Grounding DINO and SAM achieved 0.90 mIoU.

RGB-based models are effective for textile sorting tasks.

Abstract

Automated sorting is crucial for improving the efficiency and scalability of textile recycling, but accurately identifying material composition and detecting contaminants from sensor data remains challenging. This paper investigates the use of standard RGB imagery, a cost-effective sensing modality, for key pre-processing tasks in an automated system. We present computer vision components designed for a conveyor belt setup to perform (a) classification of four common textile types and (b) segmentation of non-textile features such as buttons and zippers. For classification, several pre-trained architectures were evaluated using transfer learning and cross-validation, with EfficientNetB0 achieving the best performance on a held-out test set with 81.25\% accuracy. For feature segmentation, a zero-shot approach combining the Grounding DINO open-vocabulary detector with the Segment Anything Model (SAM) was employed, demonstrating excellent performance with a mIoU of 0.90 for the generated masks against ground truth. This study demonstrates the feasibility of using RGB images coupled with modern deep learning techniques, including transfer learning for classification and foundation models for zero-shot segmentation, to enable essential analysis steps for automated textile recycling pipelines.