Deep Vision-based Smart Waste Sorting Using Vgg16 And Yolo For Real-time Applications

Abstract

Waste Classification Is A Critical Component Of Effective Waste Management, As It Enables Materials To Be Properly Segregated For Disposal, Recycling, And Environmental Protection. Traditional Waste Sorting Methods Largely Depend On Manual Labor, Which Is Not Only Time-consuming But Also Prone To Errors, Leading To Contamination Of Recyclable Materials And Inefficiencies In Waste Processing. The Rapid Increase In Global Waste Generation Has Highlighted The Urgent Need For Automated, Accurate, And Scalable Solutions. This Project Proposes A Smart Waste Classification System That Leverages Deep Learning Techniques, Specifically The VGG16 Convolutional Neural Network (CNN) Architecture, To Automatically Categorize Waste Materials Into Classes Such As Paper, Plastic, Glass, Metal, And Organic Waste. The System Uses Extensive Image Preprocessing And Augmentation Techniques To Enhance Model Performance And Robustness. In Addition, The Project Integrates The YOLO (You Only Look Once) Framework For Real-time Object Detection, Allowing The System To Efficiently Recognize And Classify Waste Items In Dynamic Environments. By Combining VGG16 For Feature Extraction And YOLO For Detection, The System Significantly Reduces Human Intervention, Improves Sorting Accuracy, And Accelerates The Recycling Process

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