afatihakcan / yolov5_pytorch_ros

Real-time object detection with ROS, based on YOLOv3 and PyTorch

yolov5_pytorch_ros

This package provides a ROS wrapper for YOLOv5 based on PyTorch-YOLOv5.

Authors: Vasileios Vasilopoulos ([e-mail]), Georgios Pavlakos ([e-mail]) Adapted by: Raghava Uppuluri

Prerequisites

1. Have conda installed
2. Have ROS installed + setup

Quick Start

1. Download the prerequisites for this package, navigate to the package folder and run:

# Ensure your conda environment is activated
conda install -f requirements.txt

Installation

Clone the repo into your src folder of your catkin_ws.

git clone https://github.com/raghavauppuluri13/yolov5_pytorch_ros.git

Navigate to your catkin workspace and run:

catkin build yolov5_pytorch_ros
# adds package to your path
source ~/catkin_ws/devel/setup.bash 

Basic Usage

To maximize portability, create a separate package and launch file. Add your weights into a weights folder of that package.

catkin_create_pkg my_detector
mkdir weights
mkdir launch
# Add weights
# Don't forget to build and source after

Then, add the following to mydetector.launch in the launch folder:

<launch>
  <include file="$(find yolov5_pytorch_ros)/launch/detector.launch">
    <!-- Camera topic and weights, config and classes files -->
    <arg name="image_topic"	                value="/camera/image_raw"/>
    <!-- Absolute path to weights file (change this) -->
    <arg name="weights_name"	            value="$(find my_detector)/weights/weights.pt"/>

    <!-- Published topics -->
    <arg name="publish_image"	            value="true"/>
    <arg name="detected_objects_topic"      value="detected_objects_in_image"/>
    <arg name="detections_image_topic"      value="detections_image_topic"/>

    <!-- Detection confidence -->
    <arg name="confidence"                  value="0.7"/>
  </include>
</launch>

Finally, run the detector:

roslaunch my_detector mydetector.launch

Should get something like this when viewed from rviz

Node parameters

• image_topic (string)

  Subscribed camera topic.

• weights_name (string)

  Weights to be used from the models folder.

• publish_image (bool)

  Set to true to get the camera image along with the detected bounding boxes, or false otherwise.

• detected_objects_topic (string)

  Published topic with the detected bounding boxes.

• detections_image_topic (string)

  Published topic with the detected bounding boxes on top of the image.

• confidence (float)

  Confidence threshold for detected objects.

Subscribed topics

• image_topic (sensor_msgs::Image)

  Subscribed camera topic.

Published topics

• detected_objects_topic (yolov3_pytorch_ros::BoundingBoxes)

  Published topic with the detected bounding boxes.

• detections_image_topic (sensor_msgs::Image)

  Published topic with the detected bounding boxes on top of the image (only published if publish_image is set to true).

Citing

The YOLO methods used in this software are described in the paper: You Only Look Once: Unified, Real-Time Object Detection.

If you are using this package, please add the following citation to your publication:

@misc{vasilopoulos_pavlakos_yolov3ros_2019,
  author = {Vasileios Vasilopoulos and Georgios Pavlakos},
  title = {{yolov3_pytorch_ros}: Object Detection for {ROS} using {PyTorch}},
  howpublished = {\url{https://github.com/vvasilo/yolov3_pytorch_ros}},
  year = {2019},
}