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

Received Date: 2026-01-22
Accepted Date: 2026-04-17
Rev Recd Date: 2026-04-16

Available Online: 2026-04-30

Abstract

Abstract

Objective Accurate plastic identification remains challenging in waste household appliance recycling under low-resolution spectral conditions. In practical recycling environments, plastics often have complex compositions, surface contamination, and aging effects, which increase classification difficulty. Black plastics are especially difficult to identify because their strong light absorption and spectral overlap in the Visible-Near Infrared (Vis-NIR) range reduce feature separability and degrade classification performance. Under these conditions, conventional single-stage classification models often fail to maintain stable accuracy. To address this problem, an automated identification method is proposed for low-dimensional multispectral feature spaces. The method aims 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 in 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 are collected from five typical household appliance plastics, including Acrylonitrile Butadiene Styrene (ABS), High-Impact PolyStyrene (HIPS), PolyPropylene (PP), Acrylonitrile Styrene copolymer (AS), and Polycarbonate/Acrylonitrile Butadiene Styrene (PC+ABS) blends. These samples are obtained from waste household appliances and are subjected to preliminary surface cleaning before spectral acquisition. To improve feature representation, a multi-level feature engineering strategy is adopted. This strategy 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 the representation of reflectance variations, whereas 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 interclass separability. To address spectral overlap and class imbalance, a Hierarchical Joint Classifier (HJC) is constructed. HJC uses a two-stage classification framework. In the first stage, an XGBoost-based primary classifier performs coarse classification to separate easily distinguishable samples and group spectrally similar black plastics. In the second stage, a TabTransformer-based secondary classifier performs 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. Accuracy, precision, recall, and F1-score are calculated from 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% in 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 provides higher performance and greater stability under low-resolution spectral conditions. Comparative results show that the proposed method outperforms baseline approaches, such as PCA combined with CNN, which achieves an accuracy of approximately 71.3% on the same dataset (Fig. 8). This improvement indicates that the proposed feature engineering strategy effectively strengthens the discriminative capability of low-dimensional spectral data. Combining LDA with feature engineering further improves class separability compared with conventional PCA-based methods. Confusion matrix analysis shows that misclassifications mainly occur between spectrally similar black ABS and black HIPS samples, whereas most other categories achieve high classification accuracy (Fig. 9). These results indicate that spectral overlap remains the main challenge under low-resolution conditions. The hierarchical classification strategy reduces this problem by focusing classification resources on difficult samples, thereby improving the overall generalization ability of the model. Overall, the proposed method shows robustness under practical conditions, including spectral noise, limited channel resolution, and material heterogeneity. These results indicate 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 spectral-shape features are incorporated into a two-stage framework to improve the identification of spectrally similar materials. The results show stable accuracy across different plastic types. The method is suitable for automated sorting in waste household appliance recycling and can be extended 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)

References

[1]	Organisation for Economic Co-operation and Development (OECD). Global plastics outlook: Policy scenarios to 2060[R]. 2022. (查阅网上资料, 未找到本条文献的报告编号, 请核对).
[2]	United Nations Environment Programme (UNEP). From pollution to solution: A global assessment of marine litter and plastic pollution[R]. 2021. (查阅网上资料, 未找到本条文献的报告编号, 请核对).
[3]	蔡毅, 田晖, 谢淼雪. 废旧家用电器塑料资源化利用及发展趋势[J]. 中国塑料, 2021, 35(8): 77–83. doi: 10.19491/j.issn.1001-9278.2021.08.010. CAI Yi, TIAN Hui, and XIE Miaoxue. Utilization and development trend of waste plastics from household electrical appliances as resources[J]. China Plastics, 2021, 35(8): 77–83. doi: 10.19491/j.issn.1001-9278.2021.08.010.
[4]	ZHENG Xuqing, DENG Mike, JIA Hao, et al. Surface modification of intumescent flame retardant and its application in polypropylene with excellent fire performance and water resistance[J]. Polymers, 2025, 17(3): 399. doi: 10.3390/polym17030399.
[5]	DENG Mike, ZHANG Zhihao, SUN Jun, et al. Improving the flame retardancy and water resistance of polypropylene by introducing microcapsule flame retardant system and modified zinc oxide[J]. Polymer Degradation and Stability, 2024, 221: 110668. doi: 10.1016/j.polymdegradstab.2024.110668.
[6]	DE OLIVEIRA SILVA R J, GRAF K, and LEITE RIBEIRO OKIMOTO M L. Plastic waste recycling: An overview of the mechanical, chemical, and thermal technologies[J]. Journal of Engineering and Applied Science, 2025, 72(1): 251. doi: 10.1186/s44147-025-00799-2.
[7]	LUBONGO C, BIN DAEJ M A A, and ALEXANDRIDIS P. Recent developments in technology for sorting plastic for recycling: The emergence of artificial intelligence and the rise of the robots[J]. Recycling, 2024, 9(4): 59. doi: 10.3390/recycling9040059.
[8]	ZHENG Yan, BAI Jiarui, XU Jingna, et al. A discrimination model in waste plastics sorting using NIR hyperspectral imaging system[J]. Waste Management, 2018, 72: 87–98. doi: 10.1016/j.wasman.2017.10.015.
[9]	KISSEL A, NOGOWSKI A, KIENLE A, et al. Flow Raman spectroscopy for the detection and identification of small microplastics[J]. Sensors, 2025, 25(5): 1390. doi: 10.3390/s25051390.
[10]	VÁZQUEZ-GUARDADO A, MONEY M, MCKINNEY N, et al. Multi-spectral infrared spectroscopy for robust plastic identification[J]. Applied Optics, 2015, 54(24): 7396–7405. doi: 10.1364/AO.54.007396.
[11]	胡锡敦, 尹禄, 杨钦晨, 等. 基于近红外高光谱成像技术的塑料分类(特邀)[J]. 激光与光电子学进展, 2024, 61(2): 0211031. doi: 10.3788/LOP232402. HU Xidun, YIN Lu, YANG Qinchen, et al. Classification of plastics based on near-infrared hyperspectral imaging technology (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(2): 0211031. doi: 10.3788/LOP232402.
[12]	SINGH A R, NEO E R K, LAI C M, et al. Deep learning-based plastic classification using spectroscopic data[J]. Journal of Cleaner Production, 2025, 530: 146793. doi: 10.1016/j.jclepro.2025.146793.
[13]	赵凤, 耿苗苗, 刘汉强, 等. 卷积神经网络与视觉Transformer联合驱动的跨层多尺度融合网络高光谱图像分类方法[J]. 电子与信息学报, 2024, 46(5): 2237–2248. doi: 10.11999/JEIT231209. ZHAO Feng, GENG Miaomiao, LIU Hanqiang, et al. Convolutional neural network and vision transformer-driven cross-layer multi-scale fusion network for hyperspectral image classification[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2237–2248. doi: 10.11999/JEIT231209.
[14]	VAN HOORN H, POURMOHAMMADI F, DE LEEUW A W, et al. Machine learning-based identification of plastic types using handheld spectrometers[J]. Sensors, 2025, 25(12): 3777. doi: 10.3390/s25123777.
[15]	MARTINEZ-HERNANDEZ U, WEST G, and ASSAF T. Low-cost recognition of plastic waste using deep learning and a multi-spectral near-infrared sensor[J]. Sensors, 2024, 24(9): 2821. doi: 10.3390/s24092821.
[16]	RANI M, MARCHESI C, FEDERICI S, et al. Miniaturized near-infrared (MicroNIR) spectrometer in plastic waste sorting[J]. Materials, 2019, 12(17): 2740. doi: 10.3390/ma12172740.
[17]	蔡轶珩, 谭美伶, 潘建军, 等. 基于多尺度非对称密集网络的高光谱图像分类[J]. 电子与信息学报, 2024, 46(4): 1448–1457. doi: 10.11999/JEIT230651. CAI Yiheng, TAN Meiling, PAN Jianjun, et al. Hyperspectral image classification based on multi-scale asymmetric dense network[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1448–1457. doi: 10.11999/JEIT230651.
[18]	刘晓敏, 余梦君, 乔振壮, 等. 面向多源遥感数据分类的尺度自适应融合网络[J]. 电子与信息学报, 2024, 46(9): 3693–3702. doi: 10.11999/JEIT240178. LIU Xiaomin, YU Mengjun, QIAO Zhenzhuang, et al. Scale adaptive fusion network for multimodal remote sensing data classification[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3693–3702. doi: 10.11999/JEIT240178.
[19]	肖振久, 田昊, 张杰浩, 等. 融合动态特征增强的遥感建筑物分割[J]. 光电工程, 2025, 52(3): 240231. doi: 10.12086/oee.2025.240231. XIAO Zhenjiu, TIAN Hao, ZHANG Jiehao, et al. Fusion of dynamic features enhances remote sensing building segmentation[J]. Opto-Electronic Engineering, 2025, 52(3): 240231. doi: 10.12086/oee.2025.240231.
[20]	吕鹏远, 兰金江, 曾学仁, 等. 基于特征增强与融合的红外目标检测算法[J]. 红外技术, 2024, 46(7): 782–790. LÜ Pengyuan, LAN Jinjiang, ZENG Xueren, et al. Infrared object detection algorithm based on feature enhancement and fusion[J]. Infrared Technology, 2024, 46(7): 782–790.