Image-Only Automated Garment Sorting for Textile Reuse and Recycling Using a Multi-Model AI Framework

The textile and clothing value chain faces increasing pressure to improve reuse and recycling rates, particularly in post-consumer scenarios where garments must be rapidly assessed, classified, and routed toward appropriate end-of-life pathways. Manual sorting remains labor-intensive and difficult to scale, especially when garments must be evaluated according to visual condition, type, color, fiber-related characteristics, and reuse potential. This article proposes and evaluates an AI-based framework for automated clothing sorting to support textile reuse and recycling. The proposed framework combines YOLO-based object detection with a Vision-Language Model for semantic interpretation in identifying garments, interpreting visual attributes, and supporting sorting decisions. The framework first distinguishes reusable garments from non-reusable items and then further classifies, using two ConvNeXt-based visual classifiers, non-reusable textiles according to characteristics relevant to recycling. By integrating computer vision and vision-language reasoning, the approach aims to improve the speed, consistency, and scalability of garment classification in circular textile systems. The results demonstrate the potential of AI-assisted sorting as a decision-support tool for increasing the recovery value of post-consumer clothing and reducing the amount of textile waste directed to landfill or incineration.