Computer Vision Projects
Explore guided student projects in Computer Vision. Build hands-on skills with real aerospace tools and data.
4 projects
Combine with other filters →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 → 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/MLCount Aircraft on a Runway with YOLO
Use a real-time object detector to automatically count planes in satellite imagery.
Run a pretrained YOLOv8 model on airport satellite images to detect and count aircraft. Learn how to use one of the most powerful object detection frameworks in computer vision without training from scratch—a key industry skill for remote sensing and airspace monitoring.
Start Project → AI/MLEdge-Deployed Satellite Detection with YOLO
Train, quantize, and deploy a YOLO model for real-time satellite detection on Jetson Nano
Train a YOLO object detection model on satellite image datasets, apply TensorRT quantization to reduce model size and inference time, and deploy on a Jetson Nano edge device for real-time detection. Demonstrates the full ML deployment pipeline from training to embedded hardware.
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