@inproceedings{d500549c047649e6bc323f491434af9c,
title = "Plastic Material Identification for Recycling Using 1D SWIR Spectral Data with AutoEncoder-Assisted XGBoost Classification",
abstract = "This paper presents a hybrid machine learning framework for plastic classification using a custom-developed short-wave infrared (SWIR) spectrometer based on a 1D spectral image sensor. To address the challenge of misclassifying out-of-distribution (OOD) data in real-world recycling scenarios, the system integrates an AutoEncoder-based OOD detector with an XGBoost classifier. The AutoEncoder filters out anomalous samples by measuring reconstruction error, while the XGBoost model classifies in-distribution (ID) spectral data into predefined plastic types. Experimental results demonstrate that the proposed system achieves a high ID F1-score of 0.95 and improves OOD detection F1-score from 0.78 to 0.98 when the AutoEncoder is applied. The results confirm the effectiveness of the proposed architecture in enhancing classification reliability and robustness, making it suitable for deployment in automated plastic recycling processes.",
keywords = "AutoEncoder, Machine Learning, Out-of-Distribution (OOD) Detection, Plastic Classification, Short-Wave Infrared (SWIR), Spectral Analysis, Spectrometer, XGBoost",
author = "Hwang, \{Ki Hyun\} and Lee, \{Chang Sug\} and Lee, \{Sang Jun\} and Kim, \{Hyeon June\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE SENSORS ; Conference date: 19-10-2025 Through 22-10-2025",
year = "2025",
doi = "10.1109/SENSORS59705.2025.11330513",
language = "English",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE SENSORS 2025 - Conference Proceedings",
}