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.
Abstract (translated)
自动化分拣对于提高纺织品回收的效率和规模至关重要，但准确识别材料组成并从传感器数据中检测污染物仍然是一个挑战。本文研究了使用标准RGB图像（一种成本效益高的传感模式）在自动系统中的关键预处理任务的应用。我们提出了一种计算机视觉组件，专门用于传送带设置，以执行(a)四种常见纺织品类型的分类和(b)非纺织特征（如纽扣和拉链）的分割。 对于分类任务，使用迁移学习和交叉验证评估了几种预训练架构，在保留的测试集上，EfficientNetB0模型实现了最佳性能，准确率为81.25%。对于特征分割，采用了一种零样本方法，结合了Grounding DINO开放词汇检测器与Segment Anything Model（SAM），生成的掩码在与真实标签对比时达到了mIoU为0.90的优秀性能。 这项研究表明，通过结合现代深度学习技术——包括迁移学习用于分类和基础模型用于零样本分割——利用RGB图像可以实现自动化纺织品回收管道所需的关键分析步骤。这种方法展示了使用RGB图像进行有效分拣的可能性，并为进一步提高效率提供了途径。
URL
https://arxiv.org/abs/2506.06569
