YOLO-DetectionROS2

YOLO-DetectionROS2 is a ROS2 package that integrates YOLOv5 and YOLOv12 for real-time object detection in robotics applications.
It provides ROS2 nodes for running detection, publishing annotated images, and streaming camera/video input for testing and deployment.

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🚀 Features

• YOLOv5 and YOLOv12 integration with ROS2.
• Real-time object detection from webcam or video source.
• Publishes annotated images with bounding boxes and FPS overlay.
• ROS2-compatible topics for detection results.
• Includes an image publisher node for testing with camera/video input.

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📦 Requirements

• ROS2 (Foxy, Humble, or newer)
• Python 3.8+
• OpenCV (cv2)
• PyTorch (torch)
• Ultralytics YOLO (pip install ultralytics)
• cv_bridge and sensor_msgs (ROS2 dependencies)
• vision_msgs (for extended detection output with YOLOv12)

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▶️ Usage

Run YOLOv5 Detector

ros2 run yolov5_detector yolov5_detector_node

Run YOLOv12 Detector

ros2 run yolov12_detector yolov12_detector_node

Run Image Publisher (Webcam/Video)

ros2 run yolov5_detector image_publisher_node

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🔧 Parameters

Each detector node supports configurable parameters:

• weights: Path to YOLO model weights (.pt file).
• imgsz: Input image size (default: 640 for YOLOv5, 480 for YOLOv12).
• conf_thres: Confidence threshold (default: 0.25).
• source: Camera index or video file path (YOLOv12).

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📊 Example Topics

• /yolov5/detections -- YOLOv5 detection results.
• /yolov12/detections -- YOLOv12 detection results.
• /yolov12/annotated_image -- Annotated images from YOLOv12.
• /image -- Raw images from image_publisher_node.

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🤖 Applications

• Robotics perception and navigation
• Autonomous vehicles and drones
• Surveillance and monitoring systems
• Real-time object recognition for research

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.