用RGB图像+AI模型实现纺织品自动分拣与杂质识别
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models
- 用迁移学习在流水线图像上分类四种常见面料
- 零样本模型组合实现90%的分割精度，精准识别纽扣拉链
- 适合做环保回收自动化系统的研发人员参考
自动化分拣对提升纺织品回收效率至关重要，但通过传感器数据准确识别材料成分和污染物仍具挑战。本文研究使用低成本的RGB图像，完成自动化系统中的关键预处理任务。设计了适用于传送带场景的计算机视觉模块，实现（a）四种常见纺织品类型的分类，（b）非纺织特征（如纽扣、拉链）的分割。分类任务中，采用迁移学习与交叉验证评估多个预训练模型，EfficientNetB0在预留测试集上达到81.25%准确率。分割任务采用结合Grounding DINO与Segment Anything Model（SAM）的零样本方法，生成掩码的mIoU达0.90，接近真实标注。结果表明，结合现代深度学习技术——迁移学习用于分类、基础模型用于零样本分割，可有效支撑自动化纺织品回收流程的关键分析步骤。
原文摘要 · Abstract (English)
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.
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