Object Detection Using Deep Neural Networks for Airplane Image Detection

Object detection is a computer vision technique that identifies, classifies and locate a particular object in a particular setting. It has become increasingly important in recent years due to its wide range of applications, including advanced driver assistance systems (ADAS), video surveillance, and image retrieval systems. In this report, we will provide an overview of common state-of-the-art object detectors and highlight differences in approach. We will also discuss the practical part of our project, including how we trained our object detector to recognize airplanes using a dataset we created. Our project aims to develop an object detection system that can accurately detect airplanes in satellite images. We will use a deep learning approach to train our model and evaluate its performance using various metrics such as precision, recall, and F1 score.