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Image Recognition for Garbage Classification Based on Pixel Distribution Learning

Jenil Kanani

TL;DR

This study proposes a novel approach inspired by pixel distribution learning techniques to enhance automated garbage classification, which aims to address limitations of conventional convolutional neural network (CNN)-based approaches, including computational complexity and vulnerability to image variations.

Abstract

The exponential growth in waste production due to rapid economic and industrial development necessitates efficient waste management strategies to mitigate environmental pollution and resource depletion. Leveraging advancements in computer vision, this study proposes a novel approach inspired by pixel distribution learning techniques to enhance automated garbage classification. The method aims to address limitations of conventional convolutional neural network (CNN)-based approaches, including computational complexity and vulnerability to image variations. We will conduct experiments using the Kaggle Garbage Classification dataset, comparing our approach with existing models to demonstrate the strength and efficiency of pixel distribution learning in automated garbage classification technologies.

Image Recognition for Garbage Classification Based on Pixel Distribution Learning

TL;DR

This study proposes a novel approach inspired by pixel distribution learning techniques to enhance automated garbage classification, which aims to address limitations of conventional convolutional neural network (CNN)-based approaches, including computational complexity and vulnerability to image variations.

Abstract

The exponential growth in waste production due to rapid economic and industrial development necessitates efficient waste management strategies to mitigate environmental pollution and resource depletion. Leveraging advancements in computer vision, this study proposes a novel approach inspired by pixel distribution learning techniques to enhance automated garbage classification. The method aims to address limitations of conventional convolutional neural network (CNN)-based approaches, including computational complexity and vulnerability to image variations. We will conduct experiments using the Kaggle Garbage Classification dataset, comparing our approach with existing models to demonstrate the strength and efficiency of pixel distribution learning in automated garbage classification technologies.
Paper Structure (15 sections, 3 equations, 4 figures, 2 tables)

This paper contains 15 sections, 3 equations, 4 figures, 2 tables.

Table of Contents

  1. Abstract
  2. Introduction
  3. Related Work
  4. Proposed Method
  5. Dataset
  6. Model Architecture
  7. Experimental setup
  8. Experiment A: Training on Original Images
  9. Experiment B: Training on Augmented Images
  10. Experiment C: Training on Shuffled Patch Images
  11. Results and Discussion
  12. Baseline Model Performance
  13. Data Augmentation Techniques
  14. Patch Shuffling Techniques
  15. Discussion

Figures (4)

  • Figure 1: Sample images of each class in the Kaggle Garbage Classification dataset
  • Figure 2: Experimental Pipeline: (a) Original images, (b) Augmented images with random transformations, (c) Images shuffled by patches of size $4 \times 4$ and $32 \times 32$.
  • Figure 3: Comparison of Original and Shuffled Images for Garbage Classification
  • Figure 4: Classification results of the model trained on (a) original images, (b) augmented images, and (c) shuffled images