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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.

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|April 13, 2023
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Summary
This summary is machine-generated.

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.

Keywords:
DCGANYOLOv4data augmentationtarget detectionwaste classification

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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.