Аналіз сучасних рішень для автоматизованої класифікації твердих побутових відходів
| dc.contributor.author | Motornyi, A. P. | en |
| dc.contributor.author | Kabachii, V. V. | en |
| dc.date.accessioned | 2026-09-03T10:41:53Z | |
| dc.date.available | 2026-09-03T10:41:53Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | The article examines modern methods for automatic classification of municipal solid waste, including visual, spectral, and hybrid approaches. It identifies key limitations of computer vision, NIR/MIR spectroscopy, and existing data-integration techniques. The need for transitioning to multimodal architectures is substantiated. A two-stage accuracy improvement strategy is proposed: optimized visual models based on ResNet-50/EfficientNet and fully integrated multimodal systems with attention modules. The paper outlines future development prospects, including the creation of realistic datasets and lightweight models for real-time operation. | en |
| dc.identifier.citation | Моторний А. П., Кабачій В. В. Аналіз сучасних рішень для автоматизованої класифікації твердих побутових відходів // Матеріали Міжнародної науково-практичної інтернет-конференції «Молодь в науці: дослідження, проблеми, перспективи (МН-2026)», м. Вінниця, 22-26 червня 2026 р. Електрон. текст. дані. 2026. URI: https://conferences.vntu.edu.ua/index.php/mn/mn2026/paper/view/27203. | uk |
| dc.identifier.udc | 628.477:004.932 | |
| dc.identifier.uri | https://ir.lib.vntu.edu.ua/handle/123456789/53441 | |
| dc.language.iso | uk_UA | uk_UA |
| dc.publisher | Вінницький національний технічний університет | uk |
| dc.relation.ispartof | Матеріали Міжнародної науково-практичної інтернет-конференції «Молодь в науці: дослідження, проблеми, перспективи (МН-2026)», м. Вінниця, 22-26 червня 2026 р. | uk |
| dc.relation.references | Zhang, S., Chen, Y., Yang, Z., & Gong, H. (2021). Computer vision based two-stage waste recognitionretrieval algorithm for waste classification. Resources, Conservation and Recycling, 169, 105543. https://doi.org/10.1016/j.resconrec.2021.105543 | en |
| dc.relation.references | Liu, Y., Yao, W., Qin, F., Zhou, L., & Zheng, Y. (2023). Spectral classification of large-scale blended (micro) plastics using FT-IR raw spectra and image-based machine learning. Environmental Science & Technology, 57(16), 6656–6663. https://doi.org/10.1021/acs.est.2c08952 | en |
| dc.relation.references | Malik, M., Sharma, S., Uddin, M., Chen, C. L., Wu, C. M., Soni, P., & Chaudhary, S. (2022). Waste classification for sustainable development using image recognition with deep learning neural network models. Sustainability, 14(12), 7222. https://doi.org/10.3390/su14127222 | en |
| dc.relation.references | Varga, D. (2024). Understanding how image quality affects transformer neural networks. Signals, 5(3), 562–579. https://doi.org/10.3390/signals5030031 | en |
| dc.relation.references | Brenner, M., Reyes, N. H., Susnjak, T., & Barczak, A. L. (2023). RGB-D and thermal sensor fusion: A systematic literature review. IEEE Access, 11, 82410–82442. https://doi.org/10.1109/ACCESS.2023.3301119 | en |
| dc.relation.references | Itam, D. H., Martin, E. C., & Horsfall, I. T. (2024). Enhanced convolutional neural network methodology for solid waste classification utilizing data augmentation techniques. Waste Management Bulletin, 2(4), 184–193. https://doi.org/10.1016/j.wmb.2024.11.002 | en |
| dc.relation.references | Chomicki, A., Wоjcik, F., & Dudycz, H. (2025). Assessing the impact of dataset quality on the performance of artificial intelligence models in automatic waste classification. Procedia Computer Science, 270, 1061–1070. https://doi.org/10.1016/j.procs.2025.09.227 8. Saroth, M. A. F., Wijerathne, P. M. A. K., & Kumara, B. T. G. S. (2024, April). Automatic multi-class non-functional software requirements classification using machine learning algorithms. In Proceedings of the International Research Conference on Smart Computing and Systems Engineering (SCSE) (Vol. 7, pp. 1–6). IEEE. https://doi.org/10.1109/SCSE61872.2024.10550526 | en |
| dc.relation.references | Kazmi, M., Hafeez, B., Aftab, F., Shahid, J., & Qazi, S. A. (2023). A deep learning-based framework for visual inspection of plastic bottles. IEEE Access, 11, 125529–125542. https://doi.org/10.1109/ACCESS.2023.3329565 | en |
| dc.relation.references | Pavithra, K. C., Kumar, P., Geetha, M., & Bhandary, S. V. (2023). Comparative analysis of pre-trained resnet and densenet models for the detection of diabetic macular edema. Journal of Physics: Conference Series, 2571(1), 012006. https://doi.org/10.1088/1742-6596/2571/1/012006 | en |
| dc.relation.references | Boysen, N., Schwerdfeger, S., & Ulmer, M. W. (2023). Robotized sorting systems: Large-scale scheduling under real-time conditions with limited lookahead. European Journal of Operational Research, 310(2), 582–596. https://doi.org/10.1016/j.ejor.2023.03.037 | en |
| dc.relation.references | Nakhaei, F., Rahimi, S., & Fathi, M. (2022). Prediction of sulfur removal from iron concentrate using column flotation froth features: Comparison of k-means clustering, regression, backpropagation neural network, and convolutional neural network. Minerals, 12(11), 1434. https://doi.org/10.3390/min12111434 | en |
| dc.relation.references | Picon, A., Bereciartua-Perez, A., Eguskiza, I., Romero-Rodriguez, J., Jimenez-Ruiz, C. J., Eggers, T., & Navarra-Mestre, R. (2022). Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation. Artificial Intelligence in Agriculture, 6, 199– 210. https://doi.org/10.1016/j.aiia.2022.09.004 | en |
| dc.relation.references | Li, B., Feng, S., Zhang, J., Chen, G., Huang, S., Li, S., & Zhang, Y. (2025). Mathematics and machine learning for visual computing in medicine: Acquisition, processing, analysis, visualization, and interpretation of visual information. Mathematics, 13(11), 1723. https://doi.org/10.3390/math13111723 | en |
| dc.relation.references | Friedrich, K., Fritz, T., Koinig, G., Pomberger, R., & Vollprecht, D. (2021). Assessment of technological developments in data analytics for sensor-based and robot sorting plants based on maturity levels to improve Austrian waste sorting plants. Sustainability, 13(16), 9472. https://doi.org/10.3390/su13169472 | en |
| dc.relation.uri | https://conferences.vntu.edu.ua/index.php/mn/mn2026/paper/view/27203 | |
| dc.subject | automated waste classification | en |
| dc.subject | computer vision | en |
| dc.subject | NIR spectroscopy | en |
| dc.subject | multimodal systems | en |
| dc.subject | deeplearning | en |
| dc.subject | data augmentation | en |
| dc.subject | waste sorting | en |
| dc.title | Аналіз сучасних рішень для автоматизованої класифікації твердих побутових відходів | uk |
| dc.type | Thesis |
Файли
Контейнер файлів
1 - 1 з 1
Вантажиться...
- Назва:
- 27203.pdf
- Розмір:
- 462,14 KB
- Формат:
- Adobe Portable Document Format
Ліцензійна угода
1 - 1 з 1