| Project Overview | This B.Tech Aerospace / Aeronautical Engineering project is based on the recent research direction 'Deep Learning-Based Object Detection for UAV Aerial Surveillance'. The project focuses on applying artificial intelligence, machine learning, deep learning, computer vision, reinforcement learning, surrogate modelling, or RAG-style intelligent assistance to the Drone and UAV Projects area. Students can use the linked 2023-onward research paper/source as the academic base, then convert it into an implementation-focused final-year project with a simplified dataset, simulation model, Python workflow, dashboard, or prototype demonstration. |
|---|---|
| Research Paper Title | Deep Learning-Based Object Detection for UAV Aerial Surveillance |
| Research Paper / PDF Link | Open Paper / PDF |
| Year | 2024 |
| Project Area | Drone and UAV Projects |
| Project Type | Computer Vision UAV |
| Required Tools / Software | Python, PyTorch/TensorFlow, OpenCV, ROS/Gazebo/AirSim optional, Streamlit |
| Main Features / Working Principle | Use YOLO/CNN-based detection on aerial images/videos captured by UAVs |
| Expected Output | A UAV object-detection prototype with bounding boxes |
| Possible Add-ons | Add tracking, counting, and alert generation |
| Get Help | Get Help on WhatsApp
Message: Hi FE, I need help with "Deep Learning-Based Object Detection for UAV Aerial Surveillance" in "Aerospace / Aeronautical Engineering" |
This B.Tech aerospace project resource helps students connect a recent AI-based research direction with a practical implementation plan, tools, expected output, and possible extensions.