Domains using OpenCV
Aircraft Detection from Drone Imagery with YOLO
Train a real-time object detector to spot aircraft from above
Train a YOLO object detection model to identify aircraft in aerial and satellite imagery. Build a complete computer vision pipeline from data labeling to real-time inference.
Start Project → AI/MLMulti-Sensor Fusion for GPS-Denied Drone Navigation
Navigate a drone without GPS using cameras, IMU, and clever math
Build a sensor fusion system that lets a drone navigate without GPS. Combine visual odometry (OpenCV), inertial measurement (IMU), and optical flow to estimate position in GPS-denied environments.
Start Project → Some AI/MLTrack Objects in Drone Video
Write a Python script that follows moving objects through aerial footage automatically.
Use OpenCV's built-in tracking algorithms to detect and follow moving objects—vehicles, people, or animals—in aerial drone video. Learn the fundamentals of computer vision: color spaces, background subtraction, contour detection, and bounding-box tracking.
Start Project → AI/MLVision-Based Landing with PX4 + OpenCV
Guide a drone to a precise landing using only a camera and fiducial markers.
Build a precision landing system for a PX4-based drone using ArUco marker detection in OpenCV. A companion computer runs the vision pipeline, estimates the marker pose relative to the drone, and sends position setpoints to PX4 via MAVSDK offboard control — closing a visual servo loop that guides the drone to a 10 cm landing accuracy.
Start Project → AI/MLVision-Based Autonomous Landing with ArduPilot
Build a drone that finds and lands on a moving platform using only a camera
Develop a complete computer vision pipeline for precision autonomous landing on a moving platform. The system uses OpenCV for target detection and tracking, feeds corrections to ArduPilot via MAVLink, and achieves landing accuracy well beyond what GPS alone can provide.
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