AI-Based Solid Waste Classification

Urban environments in most countries today are struggling with the collection and management of municipal solid waste (MSW). First of all, after collecting waste, it is a big challenge to classify the MSW mix that includes yard waste, food waste, plastics, wood, metals, papers, rubber, leather, batteries, inert materials, textiles, paint containers, and many other things. 

The main obstacle to sorting is the variety of such generated solid waste. These wastes must first be properly fractionated and sorted before going through any significant treatment procedures.

Solutions for MSW classification must be technically practical, economically viable, socially and legally acceptable, and environmentally friendly. 

An AI-based solid waste classification system is developed for the segregation of solid waste. This system uses waste bins equipped with sensors to keep the level of waste in the bins. 

Secondly, a camera is deployed to take a picture of the trash dump which contains multiple waste items. The image is segmented into grids using a grid segmentation method. This image is fed into a trained deep-learning algorithm to carry out identification. A classifier further assesses the class of each waste object and the segregated waste item is managed with the help of the control unit.

The central component of the entire system is the control unit. Based on the information obtained from the edge processing module, it produces control signals. It regulates the robotic arm's motion in accordance with the degree of freedom specifications. Using this robotic arm, the system separates the item into the appropriate garbage container i.e., metal, plastic, glass, trash, etc.

This AI-based solid waste classification system provides awe-inspiring segmentation results. The deep-learning algorithm, a popular choice for image classification, gives us 96% accurate results.