Garbage Collection and Identification System

Abstract

Garbage collection and identification encompass a series of essential processes aimed at gathering, recognizing, and categorizing diverse waste materials to ensure their appropriate disposal and recycling. Considering the escalating global waste generation, it has become increasingly vital to devise effective strategies for waste management and reduction. Garbage identification can be carried out through manual inspection performed by trained personnel or by leveraging advanced technologies such as artificial intelligence (AI) and machine learning (ML). Manual inspection involves visually examining the waste and categorizing it into various types, including organic waste, plastic waste, metal waste, hazardous waste, and others. However, this method can be time-consuming and subjective, hence the integration of AI and ML technologies (like CNN, DenseNet121, TrashNet etc) to automate and enhance the identification process. Recycling processes that require a lot of work might be simplified with the help of an automatic classification robot based on efficient image recognition. DenseNet121 and other convolutional neural network (CNN) models improved traditional image identification techniques at the time and were the industry standard. The effectiveness of the CNNs was evaluated using TrashNet, a well-known benchmark dataset made up of 2527 pictures containing six different sorts of waste. Ultimately, these efforts contribute to reducing waste generation and conserving valuable resources.