Qualitative recognition of waste textiles based on near-infrared spectroscopy and ModernTCN

Abstract

To address the challenge of online recognition of multi-class waste textiles in their sorting process, the Fabric-ModernTCN, a CNN architecture with strong fabric recognition performance and real-time inference capability, is proposed in this paper. The design of Fabric-ModernTCN is inspired by the state-of-the-art ModernTCN in the field of time series analysis. Using the Fabric-NIR-Dataset, a raw near-infrared spectral dataset including the spectra of 18 categories of fabrics, and its four preprocessed versions generated by applying the standard normal variate transformation, SG smoothing, min–max normalization, and arPLS baseline correction to Fabric-NIR-Dataset, respectively, five categories of Fabric-ModernTCN models are trained, and their recognition performances are evaluated. The most effective preprocessing method, SG smoothing, and its corresponding Fabric-ModernTCN model are selected. Further performance evaluation experiments of this model are conducted, demonstrating its classification accuracy of 93.28 % and F1-score of 94.47 %. The comparative experiments are conducted against two baseline models, InceptionTime and MiniRocket + MLP (MiniRocket coupled with a linear classifier). The results reveal that the Fabric-ModernTCN model outperforms both baseline models across five performance metrics: classification accuracy, precision, recall, F1-score, and inference time per sample. Specifically, the Fabric-ModernTCN model achieves improvements of 2.97 % and 1.82 % in classification accuracy, and 2.52 % and 1.31 % in F1-score, respectively, compared to the baseline models. Regarding computational efficiency, the inference time per sample of the Fabric-ModernTCN model is only 0.0025612 s, corresponding to an FPS of 390.4448, which highlights its strong real-time performance. The ablation experiment results further validate the rationality of the structural design and parameter selection of Fabric-ModernTCN.