Deep Learning based Object Tracking and Detection for Autonomous Drones using YOLOv3

Object detection plays a vital role in enabling drones to perceive and interact intelligently with their surroundings. The process involves data collection, selecting appropriate deep learning architectures for accurate detection. In our proposed system, we explore the integration of advanced deep learning techniques into autonomous drones for object detection. The trained models combined with onboard software integration efficiently predict object presence in real-time imagery. The project aims to significantly improve the accuracy and reliability of object recognition, enabling drones to distinguish between different objects and navigate their environment with increased precision. The research outcomes hold the potential to enhance drone applications in different fields such as surveillance, search and rescue.