YOLO ROS: Real-Time Object Detection for ROS

Updates made by Marcelino Almeida:

-- Fixed issue that this node would publish garbage into ROS when using with a TX1 or TX2.

-- Added semaphores to avoid processing the same image twice (the original version of this branch would be extensively using CPU/GPU reprocessing repeated images that were at a low frequency).

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 (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, 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: Real-Time Object Detection.

The YOLO packages have been tested under ROS Melodic and Ubuntu 18.04. This is research code, expect that it changes often and any fitness for a particular purpose is disclaimed.

Author: Marko Bjelonic, [email protected]

Affiliation: Robotic Systems Lab, ETH Zurich

Based on the Pascal VOC 2012 dataset, YOLO can detect the 20 Pascal object classes:

• person
• bird, cat, cow, dog, horse, sheep
• aeroplane, bicycle, boat, bus, car, motorbike, train
• bottle, chair, dining table, potted plant, sofa, tv/monitor

Based on the COCO dataset, YOLO can detect the 80 COCO object classes:

• person
• bicycle, car, motorbike, aeroplane, bus, train, truck, boat
• traffic light, fire hydrant, stop sign, parking meter, bench
• cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe
• backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket
• bottle, wine glass, cup, fork, knife, spoon, bowl
• banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake
• chair, sofa, pottedplant, bed, diningtable, toilet, tvmonitor, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush

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 YOLO V3 for ROS, please add the following citation to your publication:

M. Bjelonic "YOLO ROS: Real-Time Object Detection for ROS", URL: https://github.com/leggedrobotics/darknet_ros, 2018.

@misc{bjelonicYolo2018,
  author = {Marko Bjelonic},
  title = {{YOLO ROS}: Real-Time Object Detection for {ROS}},
  howpublished = {\url{https://github.com/leggedrobotics/darknet_ros}},
  year = {2016--2018},
}

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),

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 --recursive https://github.com/AkellaSummerResearch/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_61'.

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

-O3 -gencode arch=compute_62,code=sm_62

Download weights

The yolo-voc.weights and tiny-yolo-voc.weights are downloaded automatically in the CMakeLists.txt file. If you need to download them again, go into the weights folder and download the two pre-trained weights from the COCO data set:

cd catkin_workspace/src/darknet_ros/darknet_ros/yolo_network_config/weights/
wget http://pjreddie.com/media/files/yolov2.weights
wget http://pjreddie.com/media/files/yolov2-tiny.weights

And weights from the VOC data set can be found here:

wget http://pjreddie.com/media/files/yolov2-voc.weights
wget http://pjreddie.com/media/files/yolov2-tiny-voc.weights

And the pre-trained weight from YOLO v3 can be found here:

wget http://pjreddie.com/media/files/yolov3-voc.weights
wget http://pjreddie.com/media/files/yolov3.weights

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 darkned_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 darkned_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 darkned_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 darkned_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 darkned_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 darkned_ros/yolo_network_config/.