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All Journal Industrial and Domestic Waste Management
Kunwar, Suman
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering Environmental Science

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Managing Household Waste Through Transfer Learning Kunwar, Suman
Industrial and Domestic Waste Management Volume 4 - Issue 1 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/idwm.v4i1.408

Abstract

As the world continues to face the challenges of climate change, it is crucial to consider the environmental impact of the technologies we use. In this study, we investigate the performance and computational carbon emissions of various transfer learning models for garbage classification. We examine the MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models. Our findings indicate that the EfficientNetV2 family achieves the highest accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model requires more time and produces higher carbon emissions. ResNet50 outperforms ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger carbon footprint. We conclude that EfficientNetV2S is the most sustainable and accurate model with 96.41% accuracy. Our research highlights the significance of considering the ecological impact of machine learning models in garbage classification.
Plastic Waste Detection Using Deep Learning: Insights from the WaDaBa Dataset Kunwar, Suman; Owabumoye, Banji Raphael; Alade, Abayomi Simeon
Industrial and Domestic Waste Management Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/idwm.v5i1.580

Abstract

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.
Co-Authors Alade, Abayomi Simeon Owabumoye, Banji Raphael