{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:32:39Z","timestamp":1783524759580,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The global rise in electronic waste (e-waste), especially in semiconductor components such as circuit boards and microchips, underscores a critical need for improved recycling technology. Current industrial sorters often miss small, high-value components. This leads to the loss of precious metals and inefficient recycling processes. This paper introduces an automated detection framework for detecting semiconductor components in e-waste. It assesses ensemble learning methods that leverage the strengths of multiple YOLO (You Only Look Once) object detection models, including YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12. Three ensemble fusion strategies are systematically compared: standard Non-Maximum Suppression (NMS), voting-based strategies (Affirmative, Consensus, Unanimous), and Weighted Box Fusion (WBF) with both static and dynamic weight optimization. Our simulations demonstrate that using multiple models together is far more effective than a single model for the following reasons. 1. Higher Accuracy: The best configuration, Top-4 Consensus Voting ensemble strategy, achieved an mAP@0.5 of 59.63%, a 10.3% improvement over the best individual model (YOLOv8s, 54.04%); 2. Greater Reliability: It significantly reduced \u201cfalse negatives\u201d (missed detections), even in cluttered or crowded e-waste scenarios; 3. Enhanced Detection: While the individual YOLOv8 model is fast (taking only 62.6 ms), supporting real-time detection, the best ensemble configuration (Consensus Top-4) takes 384.9 ms, creating a trade-off between detection accuracy and speed; 4. Well-Balanced Performance: Some fusion strategies showed slight trade-offs in mAP for certain parts, but collectively achieved a 7% rise in F1-score, indicating a better balance between precision and recall. This research marks significant progress in smart recycling. Improved component identification allows for more efficient recovery of high-purity materials. This promotes a circular economy by ensuring that rare and strategic materials in electronics are reused instead of discarded.<\/jats:p>","DOI":"10.3390\/info17040322","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:48:41Z","timestamp":1774532921000},"page":"322","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ensemble Learning Using YOLO Models for Semiconductor E-Waste Recycling"],"prefix":"10.3390","volume":"17","author":[{"given":"Xinglong","family":"Zhou","sequence":"first","affiliation":[{"name":"The Graduate Center, City University of New York, New York, NY 10016, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4601-4507","authenticated-orcid":false,"given":"Sos","family":"Agaian","sequence":"additional","affiliation":[{"name":"The Graduate Center, City University of New York, New York, NY 10016, USA"},{"name":"Computer Science Department, The Graduate Center, College of Staten Island (CSI), City University of New York, New York, NY 10314, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"ref_1","unstructured":"Forti, V., Bald\u00e9, C.P., Kuehr, R., and Bel, G. (2020). The Global E-Waste Monitor 2020: Quantities, Flows, and the Circular Economy Potential, International Telecommunication Union."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Selvakumar, S., Adithe, S., Isaac, J.S., Pradhan, R., Venkatesh, V., and Boopathi, S. (2023). A study of the printed circuit board (PCB) e-waste recycling process. Sustainable Approaches and Strategies for E-Waste Management and Utilization, IGI Global.","DOI":"10.4018\/978-1-6684-7573-7.ch009"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mohsin, M., Rovetta, S., Masulli, F., and Cabri, A. (2025). Artificial Intelligence Approach for Waste-Printed Circuit Board Recycling: A Systematic Review. Computers, 14.","DOI":"10.3390\/computers14080304"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Luo, S., Wan, F., Lei, G., Xu, L., Ye, Z., Liu, W., Zhou, W., and Xu, C. (2024). EC-YOLO: Improved YOLOv7 model for PCB electronic component detection. Sensors, 24.","DOI":"10.3390\/s24134363"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Du, B., Wan, F., Lei, G., Xu, L., Xu, C., and Xiong, Y. (2023). YOLO-MBBi: PCB surface defect detection method based on enhanced YOLOv5. Electronics, 12.","DOI":"10.3390\/electronics12132821"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tang, J., Liu, S., Zhao, D., Tang, L., Zou, W., and Zheng, B. (2023). PCB-YOLO: An improved detection algorithm of PCB surface defects based on YOLOv5. Sustainability, 15.","DOI":"10.3390\/su15075963"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yin, X., Zhao, Z., and Weng, L. (2025). MAS-YOLO: A lightweight detection algorithm for PCB defect detection based on improved YOLOv12. Appl. Sci., 15.","DOI":"10.3390\/app15116238"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_10","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_11","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_12","unstructured":"Jocher, G. (2026, February 01). Ultralytics YOLOv5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_13","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_15","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2026, February 01). Ultralytics YOLOv8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (2024). YOLOv9: Learning what you want to learn using programmable gradient information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_17","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-time end-to-end object detection. arXiv."},{"key":"ref_18","unstructured":"Jocher, G., and Qiu, J. (2026, February 01). Ultralytics YOLO11. Available online: https:\/\/docs.ultralytics.com\/models\/yolo11."},{"key":"ref_19","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-centric real-time object detectors. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, W.C., Wang, C.C., Tsai, M.C., Huang, C.Y., Lin, C.C., and Tseng, M.H. (2025). A YOLO Ensemble Framework for Detection of Barrett\u2019s Esophagus Lesions in Endoscopic Images. Diagnostics, 15.","DOI":"10.3390\/diagnostics15182290"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tsai, C.M., Wu, L.L., and Chen, T.Y. (2025, January 19\u201320). Enhanced fisheye object detection via yolo ensemble learning and weighted box fusion. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Honolulu, HI, USA.","DOI":"10.1109\/ICCVW69036.2025.00552"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhou, B., Liu, G., Lian, D., and Zhang, R. (2022, January 17\u201322). Yolo-based multi-model ensemble for plastic waste detection along railway lines. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883308"},{"key":"ref_23","first-page":"343","article-title":"Heterogeneous ensemble approaches for robust face mask detection in crowd scenes","volume":"2","author":"Hu","year":"2023","journal-title":"J. Comput. Cogn. Eng."},{"key":"ref_24","unstructured":"Roboflow Inc. (2025, November 14). Printed Circuit Board Dataset. Available online: https:\/\/universe.roboflow.com\/roboflow-100\/printed-circuit-board."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, L. (2006, January 20\u201324). Efficient non-maximum suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., and Davis, L.S. (2017, January 22\u201329). Soft-NMS\u2014Improving object detection with one line of code. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.593"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104117","DOI":"10.1016\/j.imavis.2021.104117","article-title":"Weighted boxes fusion: Ensembling boxes from different object detection models","volume":"107","author":"Solovyev","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"\u015eim\u015fek, M.A., Sertba\u015f, A., Sasani, H., and Din\u00e7el, Y.M. (2025). Automatic meniscus segmentation using YOLO-based deep learning models with ensemble methods in knee MRI images. Appl. Sci., 15.","DOI":"10.3390\/app15052752"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Casado-Garc\u00eda, \u00c1., and Heras, J. (2020). Ensemble methods for object detection. ECAI 2020, IOS Press.","DOI":"10.3233\/FAIA200407"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ayunts, H., Agaian, S.S., and Grigoryan, A.M. (2026). Solar Photovoltaic System Fault Classification via Hierarchical Deep Learning with Imbalanced Multi-Class Thermal Dataset. Energies, 19.","DOI":"10.3390\/en19020462"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1109\/TCE.2023.3325744","article-title":"No-reference quality metrics for image decolorization","volume":"69","author":"Ayunts","year":"2023","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"17140","DOI":"10.1109\/JSEN.2024.3380826","article-title":"PCB-vision: A multiscene rgb-hyperspectral benchmark dataset of printed circuit boards","volume":"24","author":"Arbash","year":"2024","journal-title":"IEEE Sens. 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