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Article | Industrial and Domestic Waste Management | Volume 5 - Issue 1 - 2025 | 1-11 | https://doi.org/10.53623/idwm.v5i1.580
© 2025 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Plastic Waste Detection Using Deep Learning: Insights from the WaDaBa Dataset

Author(s): Suman Kunwar 1 , Banji Raphael Owabumoye 2 , Abayomi Simeon Alade 3
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1 Faculty of Computer Science, Selinus University of Sciences and Literature, Ragusa, Italy
2 Department of Geography, Obafemi Awolowo University, Ile-Ife, Nigeria
3 Department of Physics, University of Ibadan, Oyo, Nigeria

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Industrial and Domestic Waste Management

Volume 5 - Issue 1 - 2025

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With the increasing use of plastic, the challenges associated with managing plastic waste have become more difficult, emphasizing the need for effective classification and recycling solutions. This study explored the potential of deep learning, focusing on convolutional neural networks (CNNs) and object detection models like YOLO to tackle this issue using the WaDaBa dataset. The results indicated that YOLO-11m achieved the highest accuracy (98.03%) and mAP50 (0.990), while YOLO-11n performed similarly but achieved the highest mAP50 (0.992). Lightweight models like YOLO-10n trained faster but had lower accuracy, whereas MobileNetV2 demonstrated impressive performance (97.12% accuracy) but fell short in object detection. YOLO-11n had the fastest inference time (0.2720s), making it ideal for real-time detection, while YOLO-10m was the slowest (5.9416s). Among CNNs, ResNet50 had the best inference time (1.3260s), whereas MobileNetV2 was the slowest (1.4991s). These findings suggested that by balancing accuracy and computational efficiency, these models could contribute to scalable waste management solutions. The study recommended increasing the dataset size for better generalization, enhancing augmentation techniques, and developing real-time solutions.

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SUBMITTED: 07 January 2025
ACCEPTED: 24 February 2025
PUBLISHED: 2 March 2025
SUBMITTED to ACCEPTED: 49 days
DOI: https://doi.org/10.53623/idwm.v5i1.580

Cite this article
Kunwar, S., Owabumoye, B. R., & Alade, A. S. (2025). Plastic Waste Detection Using Deep Learning: Insights from the WaDaBa Dataset. Industrial and Domestic Waste Management, 5(1), 1–11. https://doi.org/10.53623/idwm.v5i1.580
Keywords
Plastic Waste Classification Deep Learning YOLO Models WaDaBa Dataset Sustainable Waste Management
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