cv-detection

Guide ROS 2 computer vision pipelines with OpenCV, depth sensing, and YOLO.

Updated Mar 18, 2026
One-click install
npx skills add https://github.com/robotics-playground/skills --skill cv-detection
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cv-detection
Source: https://github.com/robotics-playground/skills/tree/main/skills/cv-detection
Command: npx skills add https://github.com/robotics-playground/skills --skill cv-detection

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

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.

Frequently Asked Questions about cv-detection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate OpenCV with ROS 2 for real-time image processing?▼

To integrate OpenCV with ROS 2, you use cv_bridge and image_transport to convert ROS image messages into OpenCV matrices for real-time processing. This Skill provides end-to-end guidance for building robust ROS 2 computer vision pipelines and publishing annotated result streams.

How do I run YOLO object detection in a ROS 2 environment?▼

You can run YOLO object detection in ROS 2 by subscribing to a camera image topic, processing the frames with YOLOv8 or YOLOv11, and publishing the detection results. This Skill details the model workflows for training, inference, and deployment within ROS 2 nodes.

Can I use depth sensing and point clouds with ROS 2 for 3D object detection?▼

Yes, you can use depth sensing and point clouds with ROS 2 for 3D object detection by utilizing depth data and TF frames. This Skill guides you through using depth cameras like OAK-D with depthai_ros to support navigation, inspection, and quality control tasks.

What is the best way to calibrate cameras and optimize image pipelines in ROS 2?▼

The best way to calibrate cameras and optimize image pipelines in ROS 2 is to follow structured calibration workflows and image encoding best practices. This Skill provides references for ROS 2 image pipeline optimization to ensure rapid prototyping and production deployment.

Does this ROS 2 computer vision guidance support specialized hardware like OAK-D and Jetson?▼

Yes, this ROS 2 computer vision guidance explicitly supports specialized hardware including OAK-D, Jetson, and standard cameras. It provides tailored workflows for depth sensing, YOLO-based detections, and real-time image processing across these specific hardware platforms.