cv-detection

What problem does it solve?

Solves the problem of building robust ROS 2 computer vision pipelines by providing end-to-end guidance for OpenCV integration, depth sensing, and object detection.

Core Features & Use Cases

• OpenCV integration with ROS 2 via cv_bridge and image_transport to process camera data in real-time.
• Object detection and classification using YOLO (e.g., YOLOv8/YOLOv11) and depth-camera pipelines (OAK-D, depthai_ros) for 2D/3D detections.
• Depth sensing, point clouds, and 3D visualization using depth data and TF frames to support navigation, inspection, and quality control.
• ROS 2 image pipeline optimization, calibration workflows, and visualization for rapid prototyping and production deployment.

Quick Start

Create a ROS 2 computer vision workflow that subscribes to a camera image, runs OpenCV and YOLO detections, and publishes an annotated result stream.