Next-Generation Recycling: Automated Waste Sorting Using Object Detection and Classification Models

In today’s world, appropriate waste sorting is a crucial aspect of effective waste management which is essential to the sustainability of the environment. Manual labour is a significant aspect of conventional waste sorting processes, which very often leads to human errors, inefficiencies, and is expensive to implement. With the rapid increase in waste generation, there is an increasing need for smarter and more reliable sorting systems. This project outlines a smart waste sorting system with the ability to automatically identify and sort waste items into the following categories: paper, plastic, glass, metal, and organic waste, using deep learning, specifically the VGG16 neural network. The system uses a well-balanced dataset made up of various waste items images to train the model, and makes use of image pre-processing, and augmentation to improve the weight. The system also integrates the YOLO (You Only Look Once) deep learning algorithm in order to create a real time sorting s ystem for improved functionality for practice. The collaborative use of VGG16 and YOLO not only increases accuracy, but also ensures speed and reliability. The general aim of the approach is to reduce human resources and sorting errors and promote more environmentally responsible waste disposal.