Waste management is the one of the main problems in all over the globe, presently waste materials are collected and sorted by hand, it is very time consuming and it also requires so much man power. Improper management of waste materials leads to hazards including environmental deterioration, soil contamination, water pollution, and air pollution. To solve this problem, there may be a requirement for an automatic method to aid to recognize the type of waste substances and it’s Position. Today’s technology is so sophisticated due of Artificial Intelligence and Machine Learning. These technologies may be utilized to address various real time issues, this article handles the fundamental challenge of detecting and separating the waste items like plastic, paper and metal with their location. In this article above stated issue is addressed using the Faster RCNN (Region Based Convolutional Neural Networks) model which is very much accurate compared to other algorithms like YOLO (You Look Only Once) and other similar algorithms. The model is trained on a custom dataset gathered on a mobile camera and pre-processed using Label-Img Tool. Data collected with different light conditions and in unique angles. The model is trained using Faster R-CNN identify objects and to obtain locations. This may assist individuals to keep their surroundings tidy and to become conscious of the garbage substances and to identify them. This paper has been precisely recognizing kind of items and places with higher accuracy.
