Published Paper Details:

As a result of the growing amount of waste that people are producing every minute, the world's growing garbage problem poses major hazards to human life. Only in India, almost 0.2 million tons of trash are produced each day. Due to India's lack of a widespread separation mechanism, the majority of rubbish cannot be rectified with a viable solution. In certain cities, only rage pickers can separate rubbish, but they can't do it completely. As only 30-40% garbage is separated daily. Because some wastes, such as dangerous chemical waste, medical waste, floating waste, etc., cannot be manually separated, it is necessary to separate the waste in a way that is secure, forgiving, and automatic. Therefore, aim of the paper is to present a smart, which is capable of separating wastage image dataset from kaggle.com using image processing techniques. The .NET system is a image processing framework based on the module the software is an image classification algorithm machine learning process. The project's ultimate goal is to separate garbage into four major categories: paper, food waste, plastics, and metals. The proposed CNN image segmentation & object detection based, after processing and segmentation is done, it separates waste into degradable and non-degradable waste.