yolo_ros

Docker Usage by adding to compose.yml file

To use GPU with docker while on AMD64 systems, install nvidia-container-toolkit with given instructions.

Supported platforms

System	ROS Version	Value for image	Value for device	Size	file
AMD64	Humble	ghcr.io/kalanaratnayake/yolo-ros:humble	cpu, 0, 0,1,2	5.64 GB	docker/compose.amd64.yaml
Jetson Nano	Humble	ghcr.io/kalanaratnayake/yolo-ros:humble-j-nano	cpu, 0	3.29GB	docker/compose.jnano.yaml

Docker Usage with this repository

Clone this reposiotory

mkdir -p yolo_ws/src && cd yolo_ws/src
git clone https://github.com/KalanaRatnayake/yolo_ros.git && cd ..

on AMD64

Pull the Docker image and start compose (No need to run docker compose build)

cd src/yolo_ros/docker
docker compose -f compose.amd64.yaml pull
docker compose -f compose.amd64.yaml up

To clean the system,

cd src/yolo_ros/docker
docker compose -f compose.amd64.yaml down
docker volume rm docker_yolo

on JetsonNano

Pull the Docker image and start compose (No need to run docker compose build)

cd src/yolo_ros/docker
docker compose -f compose.jnano.yaml pull
docker compose -f compose.jnano.yaml up

To clean the system,

cd src/yolo_ros/docker
docker compose -f compose.jnano.yaml down
docker volume rm docker_yolo

Native Usage

Clone this repository with and install dependencies.

git clone https://github.com/KalanaRatnayake/yolo_ros.git
git clone https://github.com/KalanaRatnayake/detection_msgs.git
cd yolo_ros
pip3 install -r requirements.txt

Build the package

If required, edit the parameters at `config/yolo_ros_params.yaml' and then at the workspace root run,

colcon build

Start the system

To use the launch file, run,

source ./install/setup.bash
ros2 launch yolo_ros yolo.launch.py

Parameter decription

ROS Parameter	Docker ENV parameter	Default Value	Description
yolo_model	YOLO_MODEL	yolov9t.pt	Model to be used. see [1] for default models and [2] for custom models
subscribe_depth	SUBSCRIBE_DEPTH	True	Whether to subscribe to depth image or not. Use if having a depth camera. A ApproximateTimeSynchronizer is used to sync RGB and Depth images
input_rgb_topic	INPUT_RGB_TOPIC	/camera/color/image_raw	Topic to subscribe for RGB image. Accepts sensor_msgs/Image
input_depth_topic	INPUT_DEPTH_TOPIC	/camera/depth/points	Topic to subscribe for Depth image. Accepts sensor_msgs/PointCloud2
publish_annotated_image	PUBLISH_ANNOTATED_IMAGE	False	Whether to publish annotated image, increases callback execution time when set to True
rgb_topic	RGB_TOPIC	/yolo_ros/rgb_image	Topic for publishing synchronized rgb images. uses sensor_msgs/Image
depth_topic	DEPTH_TOPIC	/yolo_ros/depth_image	Topic for publishing synchronized depth images. uses sensor_msgs/PointCloud2
annotated_topic	ANNOTATED_TOPIC	/yolo_ros/annotated_image	Topic for publishing annotated images uses sensor_msgs/Image
detailed_topic	DETAILED_TOPIC	/yolo_ros/detection_result	Topic for publishing detailed results uses detection_msgs/Detections
threshold	THRESHOLD	0.25	Confidence threshold for predictions
device	DEVICE	'0'	cpu for CPU, 0 for gpu, 0,1,2,3 if there are multiple GPUs

[1] If the model is available at ultralytics models, It will be downloaded from the cloud at the startup. We are using docker volumes to maintain downloaded weights so that weights are not downloaded at each startup.

[2] Uncomment the commented out YOLO_MODEL parameter line and give the custom model weight file's name as YOLO_MODEL parameter. Uncomment the docker bind entry that to direct to the weights folder and comment the docker volume entry for yolo. Copy the custom weights to the weights folder.

Latency description

Here is a summary of whether latest models work with yolo_ros node (in docker) on various platforms and the time it takes to execute a single interation of YoloROS.image_callback function. Values are measured as an average of 100 executions of the function and Input is a 640x480 RGB image at 30 fps.

Laptop -> Ryzen 9 16 core with RTX3070 mobile GPU with Ubuntu 22.04 Jetson Nano -> Overclocked with Qengineering Ubuntu 20.04 in Headless mode

Model	Laptop (amd64)	Jetson Nano
yolov10x.pt	27 ms	975 ms
yolov10l.pt	20 ms	800 ms
yolov10b.pt	17 ms	750 ms
yolov10m.pt	17 ms	650 ms
yolov10s.pt	14 ms	210 ms
yolov10n.pt	13 ms	140 ms
yolov9e.pt	34 ms	1600 ms
yolov9c.pt	21 ms	700 ms
yolov9m.pt	20 ms	500 ms
yolov9s.pt	25 ms	300 ms
yolov9t.pt	24 ms	180 ms
yolov8x.pt	28 ms	2000 ms
yolov8l.pt	19 ms	1200 ms
yolov8m.pt	16 ms	700 ms
yolov8s.pt	12 ms	300 ms
yolov8n.pt	12 ms	140 ms