Object Detection for Enhanced Visuals Using Deep Learning

Abstract

Object detection serves as a foundational aspect in computer vision, finding utility across a spectrum of applications spanning autonomous driving to security surveillance. The primary objective of this endeavor was to engineer a resilient and effective object detection framework leveraging deep learning methodologies, subsequently assessing its efficacy on real-world datasets. The project leveraged contemporary neural network architectures alongside modern tools for data preprocessing and evaluation. Keywords: Single shot Detector (SSD), Pre-Processing, Deep Learning, Image Processing, Classification.