Smart Waste Classification Using Deep Learning

Abstract

Improper waste segregation is a major cause of environmental pollution and poor recycling practices. Mixing recyclable waste, organic waste, hazardous waste, and electronic waste often leads to more landfill use and the loss of valuable recyclable materials. To tackle this problem, this paper introduces a smart waste classification system that uses deep learning techniques. Users can capture or upload images of waste, which the system processes with a YOLO-based object detection model to identify and classify the different types of waste in the image. The system detects multiple waste items and provides clear outputs, including waste type, recyclability status, confidence score, and proper disposal guidance. Additionally, a risk level indicator alerts users to potential environmental or health dangers linked to specific waste types. A confidence-based message also lets users know how reliable the detection results are. To make it more accessible, the system offers voice-based disposal instructions. This solution aims to encourage proper waste segregation, raise recycling awareness, and support sustainable waste management practices.