Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management
Abstract
1. Introduction
Literature Review
2. Materials and Methods
2.1. Materials
2.2. Methodology
2.2.1. Dataset Design
2.2.2. YOLO Model Training
Validation Metrics
- The Intersection over Union (IoU)
- Precision
- Recall
- F1-score
2.2.3. Embedded System Implementation
Energy Usage Methods
2.2.4. Model Evaluation
3. Results
3.1. Autonomy of Embedded System
3.2. Comparison of Detection Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Name | No. of Categories | No. of Images | Annotation | Image Type | Author |
---|---|---|---|---|---|
UAVVaste | 1 (garbage waste in green areas) | 772 | Segmentation | Waste capture in open fields | [26] |
Trashnet | 6 (glass, paper, cardboard, plastic, metal, general waste) | 2527 | Classification | Clean background or white background | [27] |
Waste Classification data | 2 (organic and recyclable objects) | ~25,000 | Classification | Generated by Google searches | [28] |
Garbage Classification | 12 (battery, biological, brown glass, cardboard, clothes, green glass, metal, paper, plastic, shoes, trash, white glass) | 15,150 | Classification/Detection | Combining the “clothing dataset” and the web scrapping tool | [29] |
TrashBox | 7 (medical waste, electronic waste, plastics, paper, metal, glass, cardboard) | 17,785 | Classification/Detection | Generated by the web | [30] |
Model Settings: YOLOv4 Tiny | Model Settings: YOLOv7 Tiny | Model Settings: YOLOv8 Nano | Model Settings: YOLOv9 Tiny | ||||
---|---|---|---|---|---|---|---|
Input | 640 × 640 | Input | 640 × 640 | Input | 640 × 640 | Input | 640 × 640 |
Learning rate | 0.002 | Learning rate | 0.001 | Learning rate | 0.001 | Learning rate | 0.001 |
Weight decay | 0.0002 | Optimizer | Adam | Optimizer | Adam | Optimizer | Adam |
Optimizer | Adam | Momentum | 0.937 | Momentum | 0.937 | Momentum | 0.937 |
Momentum | 0.937 | Batchsize | 32 | Batchsize | 32 | Batchsize | 32 |
Batchsize | 32 | Subdivisions | 8 | Subdivisions | 8 | Subdivisions | 8 |
Subdivisions | 8 | Total epoch | 100 | Total epoch | 100 | Total epoch | 100 |
Total iterations | 6000 |
Model | Clase | Valores | Precision | Recall | F1-Score | IoU |
---|---|---|---|---|---|---|
YOLOv9 tiny | Inorganic | TP = 0.97 FN = 0.19 FP = 0.04 | ||||
Organic | TP = 0.93 FN = 0.04 FP = 0.06 |
Model | Precision | Recall | F1-Score | IoU |
---|---|---|---|---|
YOLOv4 tiny | ||||
YOLOv7 tiny | ||||
YOLOv8 nano | ||||
YOLOv9 tiny |
Model | Input Resolution | mAP (%) | Model | Input Resolution | mAP (%) |
---|---|---|---|---|---|
Faster R-CNN [34] | ~1000 × 600 | 73.2 | SDD [35] | 513 × 513 | 78.2 |
SSD512 [34] | 512 × 512 | 76.8 | YOLO v4 Tiny | 640 × 640 | 91.7 |
SSD300 [34] | 300 × 300 | 74.3 | YOLO v7 Tiny | 640 × 640 | 94.9 |
EfficientDet [36] | 1536 × 1536 | 80.4 | YOLO v8 Nano | 640 × 640 | 97.6 |
RetinaNet [37] | 600 × 600 | 81.6 | YOLO v9 Tiny | 640 × 640 | 96.8 |
Model | CPU Usage | Inference Time (ms) | FPS | Precision | Recall | F1-Score | Model Size (MB) | mAP@0.5 |
---|---|---|---|---|---|---|---|---|
YOLO v4 Tiny | 96% | 2000 | 1 | 91.71% | 94% | 0.85 | 22.4 | 0.917 |
YOLO v7 Tiny | 98% | 1900 | 1 | 91.34% | 93.83% | 0.344 | 11.7 | 0.949 |
YOLO v8 Nano | 86% | 1800 | 1.70 | 93% | 97.34% | 0.665 | 5.96 | 0.976 |
YOLO v9 Tiny | 86% | 1800 | ~1.50 | 92.11% | 94.97% | 0.729 | 4.43 | 0.968 |
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Castro-Bello, M.; Roman-Padilla, D.B.; Morales-Morales, C.; Campos-Francisco, W.; Marmolejo-Vega, C.V.; Marmolejo-Duarte, C.; Evangelista-Alcocer, Y.; Gutiérrez-Valencia, D.E. Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management. Sustainability 2025, 17, 3523. https://doi.org/10.3390/su17083523
Castro-Bello M, Roman-Padilla DB, Morales-Morales C, Campos-Francisco W, Marmolejo-Vega CV, Marmolejo-Duarte C, Evangelista-Alcocer Y, Gutiérrez-Valencia DE. Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management. Sustainability. 2025; 17(8):3523. https://doi.org/10.3390/su17083523
Chicago/Turabian StyleCastro-Bello, Mirna, Dominic Brian Roman-Padilla, Cornelio Morales-Morales, Wilfrido Campos-Francisco, Carlos Virgilio Marmolejo-Vega, Carlos Marmolejo-Duarte, Yanet Evangelista-Alcocer, and Diego Esteban Gutiérrez-Valencia. 2025. "Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management" Sustainability 17, no. 8: 3523. https://doi.org/10.3390/su17083523
APA StyleCastro-Bello, M., Roman-Padilla, D. B., Morales-Morales, C., Campos-Francisco, W., Marmolejo-Vega, C. V., Marmolejo-Duarte, C., Evangelista-Alcocer, Y., & Gutiérrez-Valencia, D. E. (2025). Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management. Sustainability, 17(8), 3523. https://doi.org/10.3390/su17083523