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Waste Detection System Based on Data Augmentation and YOLO_EC
Jinhao Fan1,2, Lizhi Cui1,2, Shumin Fei3
1School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China.
Sensors (Basel, Switzerland)
|April 13, 2023
Summary
This study introduces an advanced waste detection system using deep learning for efficient classification. The YOLO_EC model, enhanced with generative adversarial networks for data augmentation, significantly improves waste identification accuracy.
Area of Science:
- Computer Science
- Environmental Science
- Artificial Intelligence
Background:
- Effective waste classification is crucial for sustainable development.
- Current waste datasets are limited, and traditional augmentation methods offer minor improvements.
- Accurate waste detection is essential for efficient sorting processes.
Purpose of the Study:
- To develop a fast and efficient waste detection system for sorting processes.
- To address the shortage of multi-objective waste classification datasets.
- To enhance the feature extraction capabilities of waste detection models.
Main Methods:
- Optimized Deep Convolution Generative Adversarial Networks (DCGAN) for generating diverse waste images.
- Implemented a lightweight YOLOv4 model with EfficientNet as the backbone.
- Integrated a Coordinate Attention (CA) mechanism to refine feature extraction.
Main Results:
- The proposed system effectively generates multi-objective waste images using DCGAN.
- The YOLO_EC model, incorporating EfficientNet and CA, demonstrates superior performance.
- Experimental results on the HPU_WASTE dataset show improved waste detection accuracy.
Conclusions:
- The developed data augmentation and YOLO_EC waste detection system offers a significant advancement.
- The integration of DCGAN, EfficientNet, and CA mechanisms enhances waste classification efficiency.
- This approach contributes to more effective waste management and sustainable development.
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