Garbot - Semantic Segmentation for Material Recycling and 3D Reconstruction Utilizing Robotics

Abstract

Semantic segmentation directly from the images of landfills can be utilized in the earth movers to segregate the garbage autonomously. Generally, Various segregation methods are available for garbage segregation such as IOT based waste segregation, Conveyor belt segregation in which none of them are directly from landfills. Semantic segmentation is one of the important tasks that maps the path towards the complete scene understanding. The aim of this paper is to present a smart segregation method for garbage by using semantic segmentation with DeepLab V3+ Model using the framework(Backbone model) of Xception-65 with the mean accuracy of 75.01%. This paper features the segmentation with the GarbotV1 dataset which has major classifications such as Plastic, Cart-board, Wood, Metal, Sponge. The paper also contributes a method for reconstructing the segmented images to build a 3D map and this exploits the use of earth moving vehicles to navigate autonomously by localizing the segmented objects.