Household Appliance Plastics Identification by Fusing Multi-Level Feature Enhancement and Hierarchical Classification

CHONG Penghao; ZHENG Yunlong; YANG Aosong; GUO Mengci; LI Shifeng

doi:10.11999/JEIT260084

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CHONG Penghao, ZHENG Yunlong, YANG Aosong, GUO Mengci, LI Shifeng. Household Appliance Plastics Identification by Fusing Multi-Level Feature Enhancement and Hierarchical Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260084

Citation:

CHONG Penghao, ZHENG Yunlong, YANG Aosong, GUO Mengci, LI Shifeng. Household Appliance Plastics Identification by Fusing Multi-Level Feature Enhancement and Hierarchical Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260084

Citation:

CHONG Penghao, ZHENG Yunlong, YANG Aosong, GUO Mengci, LI Shifeng. Household Appliance Plastics Identification by Fusing Multi-Level Feature Enhancement and Hierarchical Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260084

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Household Appliance Plastics Identification by Fusing Multi-Level Feature Enhancement and Hierarchical Classification

doi: 10.11999/JEIT260084 cstr: 32379.14.JEIT260084

1.
School of Management Science And Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China
2.
Hebei Provincial Cross-border E-commerce Technology Innovation Center, Hebei University of Economics and Business, Shijiazhuang 050061, China

Funds: Scientific Research Project of the Education Department of Hebei Province(QN2024142)

Received Date: 2026-01-22
Accepted Date: 2026-04-17
Rev Recd Date: 2026-04-17

Available Online: 2026-04-30

Abstract

Abstract

Objective Accurate identification of plastics in waste household appliance recycling remains challenging under low-resolution spectral conditions. In practical recycling environments, plastics often exhibit complex compositions, surface contamination, and aging effects, which increase classification difficulty. In particular, black plastics show strong light absorption and spectral overlap in the visible and near infrared (Vis–NIR) range, leading to reduced feature separability and degraded classification performance. Under such conditions, conventional single-stage classification models are often unable to maintain stable accuracy. To address this problem, an automated identification method for low dimensional multispectral feature spaces is proposed, aiming to improve the discriminative capability of limited spectral information and enhance classification accuracy for complex plastic categories. Methods A compact Vis–NIR multispectral acquisition system based on the AS7265x sensor is used to collect 18 channel reflectance data within the 410–940 nm range. A handheld acquisition device with a controlled optical structure is designed to reduce environmental interference and ensure measurement consistency (Fig.3). A total of 576 samples from five typical appliance plastics, including ABS, high-impact polystyrene (HIPS), polypropylene (PP), acrylonitrile styrene copolymer (AS), and PC/ABS blends, are collected from waste household appliances and subjected to preliminary surface cleaning prior to spectral acquisition. To improve feature representation, a multi-level feature engineering strategy is adopted. It integrates original spectral intensity features, nonlinear polynomial expansion features, and adjacent channel ratio features to characterize both global and local spectral information. The nonlinear expansion enhances reflectance variation representation, while the ratio features capture local spectral shape changes and reduce external disturbances. These features are combined into a 53-dimensional feature vector. Linear Discriminant Analysis (LDA) is then applied to enhance inter class separability. To handle spectral overlap and class imbalance, a Hierarchical Joint Classifier (HJC) is constructed. The HJC adopts a two stage classification framework: an XGBoost based primary classifier performs coarse classification to separate easily distinguishable samples and group spectrally similar black plastics, while a TabTransformer based secondary classifier is used for fine grained classification of difficult samples (Fig. 6). This hierarchical design reduces classification complexity and improves discrimination for challenging categories. Model performance is evaluated using five fold cross validation and an independent test set. Evaluation metrics, including accuracy, precision, recall, and F1 score, are calculated based on confusion matrices (Fig. 7). Comparative experiments are conducted with traditional machine learning methods, ensemble learning models, and deep learning approaches under different feature processing strategies (Fig. 8, Fig. 9). Results and Discussions The proposed HJC achieves a classification accuracy of 97.4% under five fold cross validation and 93.1% on the independent test set (Table 4). Compared with single stage classifiers and methods without feature enhancement, the proposed method shows improved performance and stability under low resolution spectral conditions. Comparative results indicate that the proposed method outperforms baseline approaches such as PCA combined with CNN, which achieves approximately 71.3% accuracy under the same dataset (Fig. 8). This improvement indicates that the proposed feature engineering strategy effectively enhances the discriminative capability of low dimensional spectral data. In addition, combining LDA with feature engineering further improves class separability compared with conventional PCA based methods. Confusion matrix analysis shows that misclassifications are mainly concentrated between spectrally similar black ABS and black HIPS samples, while other categories achieve high classification accuracy (Fig. 9). This result suggests that spectral overlap remains the main challenge under low resolution conditions. The hierarchical classification strategy mitigates this issue by focusing classification on difficult samples, thereby improving the overall generalization capability of the model. Overall, the proposed method demonstrates robustness under practical conditions, including spectral noise, limited channel resolution, and material heterogeneity, indicating its suitability for real world recycling applications. Conclusions A hierarchical classification method with multi-level spectral feature engineering is developed for plastic identification under low-resolution Vis–NIR conditions. Nonlinear and morphological features are incorporated into a two-stage framework to improve performance on spectrally similar materials. The results show consistent accuracy across different plastic types. The method is suitable for automated sorting in waste appliance recycling and can be applied to other material identification tasks with limited spectral information.
- Plastic classification,
- Visible–near infrared spectroscopy,
- Hierarchical classification,
- Feature enhancement

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References(20)

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