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. Post-consumer garment sorting must preserve reusable items while directing non-reusable textiles toward appropriate recycling or inspection pathways. This article presents a two-stage image-only decision-support framework that combines YOLO-based image classification, a locally executed vision–language model (VLM), two ConvNeXt-Tiny textile classifiers, and deterministic routing rules. In Stage 1, YOLO classifiers estimate garment type and dominant color, while Qwen2.5-VL-3B-Instruct VLM assesses visible stains, holes, pilling or lint, tags, dirt or discoloration, intentional distressing, condition, and supporting evidence. The backend validates these outputs and applies explicit precedence and uncertainty rules to assign categories A (resale), B (donation/reuse), C (recycling-oriented pre-sorting), or D (critical review). Stage 2 is triggered only for C/D garments and aggregates predictions from multiple RGB crops to estimate broad material-family hints and visible fabric structure before proposing an initial route, container, color group, recycling mechanism, and validation requirement. The YOLO garment-type classifier achieved 78.6% top-1 and 99.2% top-5 accuracy on the test set. The ConvNeXt-Tiny fabric-structure classifier achieved 78.55% accuracy and 78.64% macro-F1, whereas the material-family classifier achieved 56.39% accuracy and 55.83% macro-F1. In a controlled Stage 1 pilot test, binary reuse-oriented versus additional-processing routing achieved 80.0% accuracy, 75.0% precision, 75.0% recall, and an F1-score of 0.75. A Stage 2 end-to-end pilot test achieved 66.7% correctly recommended final routes, with macro-F1 of 0.767. These results provide evidence that complementary models and explicit validation rules can support preliminary explainable garment triage. However, RGB imagery cannot confirm exact fiber composition, blend percentages, or chemical contamination.