๐ Real-Time Drone Detection Using YOLOv5 | Deep Learning-Based IEEE Final Year Project
This project presents a real-time drone detection system powered by YOLOv5, a state-of-the-art object detection algorithm. It efficiently detects multiple flying drones in video streams using a smart surveillance camera setup. Ideal for aerial security, defense, and restricted area monitoring.
๐ Key Features of the Project:
Real-time object detection using YOLOv5 (You Only Look Once)
Pretrained YOLO weights fine-tuned on a custom drone dataset
Drone localization with bounding boxes and confidence scores
Integration with OpenCV for live video frame capture
Alert system on drone detection (visual/audio alert optional)
Deployment-ready Flask web app interface
๐ป Tools & Technologies Used:
Python, PyTorch, OpenCV
YOLOv5 (Ultralytics)
Custom drone dataset annotation using LabelImg
Flask Web App (Frontend: HTML, CSS, JS)
Live webcam or CCTV integration supported
๐ Best Suited For:
B.E / B.Tech / M.Tech / MCA Final Year Projects
IEEE Standards in AI and Deep Learning
Research in surveillance, smart cities, and airspace security
Drone detection and computer vision-based safety systems
๐ฆ Project Package Includes:
โ YOLOv5 Trained Model
โ Dataset + Annotation Files
โ Complete Source Code
โ Report + Abstract + PPT in IEEE Format
โ Flask Web Interface
โ Support for Demo + Customization
๐ฒ Contact for Code, Report & Full Project Setup
๐ +91-8088605682
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