Graduate project

Detection of Waste Materials Using Deep Learning and Image Processing

The process of segregating waste prompts the generation of energy out of waste, diminishing landfills, recycling, and reduction of waste. Erroneous disposal of waste leads to recycling contamination. Contamination is a tremendous issue to the recycling industry that can be alleviated with automatic computerized waste sorting. The presence of models or strategies which help people to sort trash has become extremely important in the right discard of that garbage. Even though there are various sorts of recycling categories, many people remain confused or cannot appropriately recognize how to decide the right trash bin to dispose of every trash. Waste management and systematic sorting of them are considered to be a significant role in ecological development around the world. Society needs to lessen waste by recycling and reusing discarded materials that result in reducing environmental problems. This project aims to create an automated waste detection system using a deep learning algorithm that will gather the waste images or videos from a camera with object recognition, detection & prediction, and categorize the waste materials like cardboard, glass, metal, paper, plastic, and trash so that the waste can be properly dumped in the recyclable and non-recyclable bin.