This project is a study about Deep Learning and Convolutional Neural Network and
their applications in the field of computer vision, specifically Object Localization and
Pose Estimation. Object localization and pose estimation play a crucial role in
various computer vision applications; including robotics, augmented reality, self
driving cars etc.
Through this project I plan to develop a basic and intuitive understanding of DL and
CNN. I investigate models such as the Segment Anything Model (SAM) and You
Only Look Once (YOLO) for object detection and segmentation, along with
geometric algorithms like RANSAC and the Perspective-n-Point (PnP) method for
accurate pose estimation. This work involves an in-depth study of various vision
models, analysing their inner workings and evaluating their impact and performance.
By combining data-driven deep learning techniques with classical computer vision
algorithms, this project aims to achieve a robust and adaptable system for precise
object localization and pose estimation. Experimental results demonstrate the
effectiveness of this hybrid approach in improving accuracy and reliability across
diverse conditions.