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Research on deep learning garbage classification system based on fusion of image classification and object detection
Zhongxue Yang1, Yiqin Bao1, Yuan Liu2
1School of information engineering, Nanjing XiaoZhuang University, Nanjing 211171, China.
Mathematical Biosciences and Engineering : MBE
|March 10, 2023
Summary
This study developed a deep learning system for effective garbage classification. By combining image classification and object detection, the system achieved a 98% recognition rate for waste identification.
Area of Science:
- Computer Science
- Environmental Science
- Artificial Intelligence
Background:
- Increasing economic development leads to higher waste generation and pollution.
- Effective garbage classification and processing are crucial for environmental protection.
- Current methods require improvement for accuracy and efficiency.
Purpose of the Study:
- To develop and evaluate a deep learning-based garbage classification system.
- To integrate image classification and object detection for enhanced waste recognition.
- To improve the accuracy of garbage image classification.
Main Methods:
- Utilized deep learning convolutional neural networks (CNNs).
- Trained and tested ResNet and MobileNetV2 for image classification.
- Employed YOLOv5 algorithms for object detection of garbage.
- Merged results from five research outcomes using a consensus voting algorithm.
Main Results:
- Achieved an approximate 98% recognition rate for garbage image classification.
- Improved image classification accuracy by 2% through consensus voting.
- Successfully transplanted the system to a Raspberry Pi microcomputer.
Conclusions:
- The developed deep learning system significantly enhances garbage image classification accuracy.
- The integrated approach of image classification and object detection is effective for waste management.
- The system's successful deployment on a Raspberry Pi demonstrates practical applicability.
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