Image Recognition for Garbage Classification Based on Pixel Distribution Learning

TL;DR

This paper introduces a pixel distribution learning approach to improve automated garbage classification, addressing CNN limitations like computational complexity and image variation sensitivity, with experiments on Kaggle dataset.

Contribution

It presents a novel pixel distribution learning method that enhances garbage classification accuracy and efficiency over traditional CNN-based techniques.

Findings

Outperforms existing models on Kaggle dataset

Reduces computational complexity

Improves robustness 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.