YOLO ROS: Real-Time Object Detection for ROS

Overview

This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package you are able to use YOLO (v2-v4) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect FSAC cones trained from FSACOCO dataset, or you can also create a network with your own detection objects. For more information about YOLO, Darknet, available training data and training YOLO see the following link: Yolo v4, v3 and v2 for Windows and Linux.

The YOLO and ROS packages are modified from darknet_ros and have been tested under ROS Melodic and Ubuntu 18.04. Compared with the original version, it has the following improvements:

• Add YOLOv4 and Tiny-YOLOv4 model.
• Support FP16 inference, up to 3x performance improvement on GPU
• Timestamp synchronization

Author: Tairan Chen, [email protected]

Affiliation: BITFSD, Beijing Institute of Technology

Installation

Dependencies

This software is built on the Robotic Operating System ([ROS]), which needs to be installed first. Additionally, YOLO for ROS depends on following software:

• OpenCV (Computer Vision Library),
• boost (C++ Library)
• CUDA (If Nvidia GPU avaliable)
• cuDNN (if Nvidia GPU avaliable)

Building

In order to install darknet_ros, clone the latest version using SSH (see how to set up an SSH key) from this repository into your catkin workspace and compile the package using ROS.

cd catkin_workspace/src
git clone [email protected]:leggedrobotics/darknet_ros.git
cd ../

To maximize performance, make sure to build in Release mode. You can specify the build type by setting

catkin_make -DCMAKE_BUILD_TYPE=Release

or using the Catkin Command Line Tools

catkin build darknet_ros -DCMAKE_BUILD_TYPE=Release

Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU ! You'll have to have an Nvidia GPU and you'll have to install CUDA. The CMakeLists.txt file automatically detects if you have CUDA installed or not. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. If you do not have CUDA on your System the build process will switch to the CPU version of YOLO. If you are compiling with CUDA, you might receive the following build error:

nvcc fatal : Unsupported gpu architecture 'compute_86'.

This means that you need to check the compute capability (version) of your GPU. You can find a list of supported GPUs in CUDA here: CUDA - WIKIPEDIA. Simply find the compute capability of your GPU and add it into darknet_ros/CMakeLists.txt. Simply add a similar line like

-gencode arch=compute_86,code=[sm_86,compute_86]

Download weights

If you need to download pre-trained weights for FSAC cone detection, go into the weights folder and download the YOLOv4-Tiny pre-trained weights from Google Drive:

cd catkin_workspace/src/darknet_ros/darknet_ros/yolo_network_config/weights/

or download from Baidu Disk with password pfg9.

Use your own detection objects

In order to use your own detection objects you need to provide your weights and your cfg file inside the directories:

catkin_workspace/src/darknet_ros/darknet_ros/yolo_network_config/weights/
catkin_workspace/src/darknet_ros/darknet_ros/yolo_network_config/cfg/

In addition, you need to create your config file for ROS where you define the names of the detection objects. You need to include it inside:

catkin_workspace/src/darknet_ros/darknet_ros/config/

Then in the launch file you have to point to your new config file in the line:

<rosparam command="load" ns="darknet_ros" file="$(find darknet_ros)/config/your_config_file.yaml"/>

Unit Tests

Run the unit tests using the Catkin Command Line Tools

catkin build darknet_ros --no-deps --verbose --catkin-make-args run_tests

You will see the image above popping up.

Basic Usage

In order to get YOLO ROS: Real-Time Object Detection for ROS to run with your robot, you will need to adapt a few parameters. It is the easiest if duplicate and adapt all the parameter files that you need to change from the darknet_ros package. These are specifically the parameter files in config and the launch file from the launch folder.

Nodes

Node: darknet_ros

This is the main YOLO ROS: Real-Time Object Detection for ROS node. It uses the camera measurements to detect pre-learned objects in the frames.

ROS related parameters

You can change the names and other parameters of the publishers, subscribers and actions inside darknet_ros/config/ros.yaml.

Subscribed Topics

• /camera_reading ([sensor_msgs/Image])

  The camera measurements.

Published Topics

• object_detector ([std_msgs::Int8])

  Publishes the number of detected objects.

• bounding_boxes ([darknet_ros_msgs::BoundingBoxes])

  Publishes an array of bounding boxes that gives information of the position and size of the bounding box in pixel coordinates.

• detection_image ([sensor_msgs::Image])

  Publishes an image of the detection image including the bounding boxes.

Actions

• camera_reading ([sensor_msgs::Image])

  Sends an action with an image and the result is an array of bounding boxes.

Detection related parameters

You can change the parameters that are related to the detection by adding a new config file that looks similar to darknet_ros/config/yolo.yaml.

• image_view/enable_opencv (bool)

  Enable or disable the open cv view of the detection image including the bounding boxes.

• image_view/wait_key_delay (int)

  Wait key delay in ms of the open cv window.

• yolo_model/config_file/name (string)

  Name of the cfg file of the network that is used for detection. The code searches for this name inside darknet_ros/yolo_network_config/cfg/.

• yolo_model/weight_file/name (string)

  Name of the weights file of the network that is used for detection. The code searches for this name inside darknet_ros/yolo_network_config/weights/.

• yolo_model/threshold/value (float)

  Threshold of the detection algorithm. It is defined between 0 and 1.

• yolo_model/detection_classes/names (array of strings)

  Detection names of the network used by the cfg and weights file inside darknet_ros/yolo_network_config/.