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Optimizing Waste Management with Advanced Object Detection for Garbage Classification

Everest Z. Kuang, Kushal Raj Bhandari, Jianxi Gao

TL;DR

This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing, and demonstrates how YOLO V5 can effectively identify various types of waste.

Abstract

Garbage production and littering are persistent global issues that pose significant environmental challenges. Despite large-scale efforts to manage waste through collection and sorting, existing approaches remain inefficient, leading to inadequate recycling and disposal. Therefore, developing advanced AI-based systems is less labor intensive approach for addressing the growing waste problem more effectively. These models can be applied to sorting systems or possibly waste collection robots that may produced in the future. AI models have grown significantly at identifying objects through object detection. This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing. The study demonstrates how YOLO V5 can effectively identify various types of waste, including plastic, paper, glass, metal, cardboard, and biodegradables.

Optimizing Waste Management with Advanced Object Detection for Garbage Classification

TL;DR

This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing, and demonstrates how YOLO V5 can effectively identify various types of waste.

Abstract

Garbage production and littering are persistent global issues that pose significant environmental challenges. Despite large-scale efforts to manage waste through collection and sorting, existing approaches remain inefficient, leading to inadequate recycling and disposal. Therefore, developing advanced AI-based systems is less labor intensive approach for addressing the growing waste problem more effectively. These models can be applied to sorting systems or possibly waste collection robots that may produced in the future. AI models have grown significantly at identifying objects through object detection. This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing. The study demonstrates how YOLO V5 can effectively identify various types of waste, including plastic, paper, glass, metal, cardboard, and biodegradables.

Paper Structure

This paper contains 8 sections, 8 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Result and Analysis
  5. Conclusion
  6. Appendix: Training Dataset
  7. Appendix: Performance with the Validation Data
  8. Appendix: Analysis of Confidence Level on Test Data

Figures (8)

  • Figure 1: Example of Garbage Detection using YOLO model
  • Figure 2: Confusion Matrix for classification of different types of garbage with background
  • Figure 3: Garbage Type Distribution for Real World Test data
  • Figure 4: Frequency of Type of Garbage based on different type of location
  • Figure 5: Correlation Matrix of a different dimension of the object recognized with the confidence level
  • ...and 3 more figures