Overview
Developed a YOLOv8-based UAV surveillance system with video analytics, risk assessment, and automated mission reporting for enhanced security monitoring.
Problem Statement
Traditional surveillance systems require constant human monitoring and lack automated threat detection. This system automates the analysis process using computer vision to detect and assess potential risks in real-time UAV footage.
System Architecture
- Object Detection: YOLOv8 model for real-time object identification
- Video Processing: Frame-by-frame analysis pipeline
- Risk Assessment: Automated threat level calculation
- Reporting: Automated mission report generation
Technologies Used
Python
YOLOv8
Computer Vision
Machine Learning
OpenCV
Challenges & Solutions
Challenge: Real-time Processing
Processing high-resolution video feeds in real-time required optimization of the YOLOv8 inference pipeline. I implemented frame skipping and multi-threading to maintain performance.
What I Learned
- Deep learning model deployment and optimization
- Computer vision pipeline design
- Real-time video processing techniques
- Automated reporting system design