INDOOR OBJECT DETECTION

Abstract

The process of identifying and localizing various items, usually in pictures that represent objects found in daily life, is called object detection. Object detection identifies each object as belonging to a specific class and creates a bounding box around it. In this thesis we focus our study in indoor datasets. The purpose of the thesis is to evaluate different methods of object detection in indoor datasets. We also aim to compare these results with each other, in order to try and find the best methods for the selected datasets. Overall, these results highlight how crucial it is to carefully evaluate model architectures, preprocessing methods, and dataset properties in order to fully utilize deep learning for 3D applications. Subsequent investigations may examine techniques to mitigate class disparities and improve model resilience in a variety of object categories and shapes.