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Received September 10, 2025
Revised November 4, 2025
Accepted November 27, 2025
Available online December 19, 2025
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폐플라스틱 재활용을 위한 최신 객체 탐지 모델의 성능 분석 : Swin transformer와 비전 모델 평가
Performance Analysis of State-of-the-Art Object Detection Models for Plastic Waste Recycling : Evaluation of Swin Transformer and Vision Models
계명대학교
Keimyung University
yuchan.ahn@kmu.ac.kr
Korean Chemical Engineering Research, February 2026, 64(1), 105148
https://doi.org/10.9713/kcer.2026.64.1.105148
https://doi.org/10.9713/kcer.2026.64.1.105148
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Abstract
본 연구는 폐플라스틱 자동 분류를 위한 최신 인공지능 객체 탐지 모델의 성능을 평가하였다 . Faster R-CNN, YOLOv8, YOLOv11, Swin Transformer 총 4개 모델을 대상으로 PET, PS, PP, PE 4종의 폐플라스틱 이미지 48,000개로 학습 및 테스트를 수행하였다 . Optuna를 활용해 하이퍼파라미터를 최적화하고 , 정확도 , 정밀도 , 재현율 , F1 score, 평균 정밀도 평균값 (mAP), 추론 시간 등으로 성능을 평가하였다 . Swin Transformer는 최고 정확도 (0.988)와 mAP(0.988)를 기록하며 복잡하거나 오염된 플라스틱에서도 우수한 분류 성능을 나타냈다 . YOLOv11은 가장 빠른 추론 속도 (61.67 ms/이미지 ) 를 보이며 실시간 처리 환경에 적합함을 확인하였다 . 연구 결과 , 모델 선택 시 정확도와 처리 속도 간의 트레이드오프 가 존재함을 확인하였으며 , 폐플라스틱 재활용을 위한 AI 기반 분류 시스템 구축에 실질적인 참고 자료를 제공한다 .
This study evaluates the performance of state-of-the-art AI-based object detection models for automated plastic waste classification. Four models—Faster R-CNN, YOLOv8, YOLOv11, and Swin Transformer—were trained and tested on a dataset of 48,000 images covering four plastic types (PET, PS, PP, PE). Hyperparameters were optimized using Optuna, and performance was assessed via accuracy, precision, recall, F1 score, mean Average Precision (mAP), and inference time. Swin Transformer achieved the highest accuracy (0.988) and mAP (0.988), demonstrating superior classification capability, particularly for complex or contaminated plastics. YOLOv11 exhibited the fastest inference speed (61.67 ms/image) with competitive accuracy, highlighting its suitability for real-time applications. These results reveal a trade-off between accuracy and processing speed, providing guidance for model selection based on application requirements. This study offers quantitative benchmarks and practical insights for deploying AI-driven plastic recycling systems.
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Korean Chemical Engineering Research 64(1) (2026) 105148
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Korean Chemical Engineering Research 64(1) (2026) 105148
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2. Dokl, M., Van Fan, Y., Vujanović, A., Pintarič, Z. N., Aviso, K. B., Tan, R. R., Pahor, B., Kravanja, Z. and Čuček, L., “A Waste Sep-
Korean Chemical Engineering Research 64(1) (2026) 105148
aration System Based on Sensor Technology and Deep Learning: A Simple Approach Applied to a Case Study of Plastic Packaging Waste,” J. Clean. Prod. 450, 141762(2024).
3. Nafiu, S. A., Ogunbusola, E. M., Adeyemi, A. G., Olatunji, T. L. and Oladimeji, A. I., “Waste Plastic Management: Recycling and the Environmental Health Nexus,” Clean. Mater. 15, 100291(2025).
4. Lubongo, C. and Alexandridis, P., “Assessment of Performance and Challenges in Use of Commercial Automated Sorting Tech- nology for Plastic Waste,” Recycling 7(2), 11(2022).
5. Muzata, T. S., Matuana, L. M. and Rabnawaz, M., “Challenges in the Mechanical Recycling and Upcycling of Mixed Postcon- sumer Recovered Plastics (PCR): A Review,” Current Research in Green and Sustainable Chemistry 8, 100407(2024).
6. Satav, A. G., Pradhan, S., Dange, S. P. and Bendre, P., “A State-of- the-Art Review on Robotics in Waste Sorting: Scope and Chal- lenges,” International Journal on Interactive Design and Manu- facturing (IJIDeM) 17(6), 2789-2806(2023).
7. Kerdlap, P., Sakulwiriyanan, P. and Gheewala, S. H., “Life Cycle Cost Analysis of Distributed versus Centralized Plastic Sorting and Recycling,” Journal of Industrial Ecology. 27(1), 297-311(2023).
8. Ruj, B., Pandey, V., Jash, P. and Srivastava, V. K., “Sorting of Plas- tic Waste for Effective Recycling,” Int. J. Appl. Sci. Eng. Res., 4(4), 564-571(2015).
9. Choi, J., Lim, B. and Yoo, Y., “Advancing Plastic Waste Classi- fication and Recycling Efficiency: Integrating Image Sensors and Deep Learning Algorithms,” Appl. Sci., 13(18), 10224(2023).
10. Jobert, G. and Brenière, X., “Mid-infrared Spectrometer for Black Plastics Sorting Using a Broadband Uncooled Micro-bolometer Array,” Spectrosc. J., 3(2), 13(2025).
11. Chen, X., Yan, L., Yin, X., Zhao, Y. and Zeng, W., “Determining the Composition of Post-consumer Flexible Multilayer Plastic Packaging with Near-infrared Spectroscopy,” Waste Manag., 123, 33-41(2021).
12. Ramos, E., Lopes, A. G. and Mendonça, F., “Application of Machine Learning in Plastic Waste Detection and Classification: A Systematic Review,” Processes 12(8), 1632(2024).
13. Fotovvatikhah, F., Bazarghan, M., Pirani, M. and Malmir, M., “A Systematic Review of AI-based Techniques for Automated Waste Classification,” Sensors 25(10), 3181(2025).
14. Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I. and Yap, P. S., “Artificial Intelligence for Waste Management in Smart Cities: A Review,” Environ. Chem. Lett., 21(4), 1959-1989 (2023).
15. 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,” Recycling 9(4), 59(2024).
16. Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M. and Parmar, M., “A Review of Convolutional Neural Networks in Computer Vision,” Artif. Intell. Rev., 57(4), 99(2024).
17. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, L. and Tong, J., “Recent Advances in Con- volutional Neural Networks,” Pattern Recognit., 77, 354-377(2018).
18. He, K., Gkioxari, G., Dollar, P. and Girshick, R., “Mask R-CNN,” Proc. IEEE Int. Conf. Comput. Vis. 2017.
19. Redmon, J., Divvala, S., Girshick, R. and Farhadi, A., “You Only Look Once: Unified, Real-time Object Detection,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2016.
20. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S. and Guo, B., “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows,” Proc. IEEE/CVF Int. Conf. Comput. Vis. 2021.
21. Wang, Z., Li, M., Zhou, Y. and Zhang, X., “Multi-category Sorting of Plastic Waste Using Swin Transformer: A Vision-based Approach,” J. Environ. Manag., 370, 122742(2024).
22. Kunwar, S., Owabumoye, B. R. and Alade, A. S., “Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset,” arXiv preprint arXiv:2412.20232 (2024).
23. Ahmed, M. I. B., Rahman, A. and Osman, S., “Deep Learning Approach to Recyclable Products Classification: Towards Sustain- able Waste Management,” Sustainability 15(14), 11138(2023).
24. Alsabbagh, A. R. and Al-Kadi, O., “Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakes,” arXiv preprint arXiv:2406.08758 (2024).
25. Ming, L. W., Foo, S. Y., Ng, A. and Yeap, G., “AI as a Driver of Efficiency in Waste Management and Resource Recovery,” Int. Trans. Artif. Intell., 2(2), 128-134(2024).
26. Chacón-Albero, O., Lopez, R. and Alvarez, M., “AI for Sustain- able Recycling: Efficient Model Optimization for Waste Classi- fication Systems,” Sensors 25(12), 3807(2025).
27. Ahn, S., Junhyeok, and Yuchan, A., “AI-based Plastic Waste Sort- ing Method Utilizing Object Detection Models for Enhanced Classification,” Waste Manag., 193, 273-282(2025).
28. AIHub, “Plastic Waste Dataset,” aihub.or.kr, https://www.aihub.or.kr/ (Accessed 2025-01-10).
29. Ren, S., He, K., Girshick, R. and Sun, J., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” Advances in Neural Information Processing Systems 28 (2015).
Korean Chemical Engineering Research 64(1) (2026) 105148
30. Hidayatullah, P., Malik, H. and Gunawan, W., “YOLOv8 to YOLO11: A Comprehensive Architecture In-Depth Comparative Review,” arXiv preprint arXiv: 2501.13400 (2025).
31. Raiaan, M. A. K., Ghosh, S., Rahman, M. and Hossain, J., “A Systematic Review of Hyperparameter Optimization Techniques in Convolutional Neural Networks,” Decis. Anal. J., 11, 100470
(2024).
32. Ultralytics, “YOLO Documentation,” docs.ultralytics.com, https:// docs.ultralytics.com/ (Accessed 2025-01-10).
33. Ghate, D., Patil, A. and Pawar, V., “Advancing Arecanut Quality Grading: A Comparative Analysis of YOLO Models with Hyperpa- rameter Optimization,” (2025).
34. Diptho, R. A. and Basak, S., “Enhancing Dermatological Diag- nosis Through Medical Image Analysis: How Effective Is YOLO11 Compared to Leading CNN Models?,” NDT 3(2), 11(2025).
35. Praveena, S. and Veeramakali, T., “Multiple Hyperparameter Optimization in Image Processing Employing Swin Transformer for Enhanced Skin Lesion Segmentation,” 2025 Int. Conf. Com- put. Robot. Test. Eng. Eval. (ICCRTEE), IEEE, 2025.
