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ROS Node: rknn_yolo_node

Overview

The rknn_yolo_node is a ROS node that uses the RKNN (Rockchip NPU Neural Networks API) model for object detection. It subscribes to an image topic, processes the images using the YOLO (You Only Look Once) object detection algorithm, and publishes the detection results.

To-do List

  • yolov5 detection
  • yolov8 detection
  • yolov8 detection with sahi
  • yolov8 segmentation
  • yolov8 pose
  • yolov8 obb

Tested Platforms

Currently only tested on rk3588, rk3588 is installed with ubuntu22.04 gnome desktop version, ros1 is installed with the o version. In ubuntu22.04, you can install ros-desktop-full-dev and most of the other packages listed at https://wiki.debian.org/DebianScience/Robotics/ROS/Packages through apt (some are in Debian, but not yet in Ubuntu, it lags behind Debian's robot technology release). You may need to compile some source code packages from https://github.com/ros-o and the official GitHub repository (such as rqt) - if there is a ros-o version that includes some patches not yet in the official repository, please use the ros-o version.

Subscribed Topics

  • raw_img_topic_name (sensor_msgs/Image): This topic is dynamically reconfigurable. The node subscribes to this topic to receive raw images for object detection. The default topic name is /camera_node/image_raw.

Published Topics

  • yolo_output_msg (vision_msgs/YoloResult): This topic publishes the results of the YOLO object detection. Each message includes the detected objects and their bounding boxes.
  • yolo_output_img_msg (sensor_msgs/Image): This topic publishes the images with the detected objects highlighted, if the enable_draw parameter is set to True.

Services

  • ~do_yolo (ros_rknn_yolo/DoYolo): This service performs YOLO object detection on a provided image and returns the detection results.

Parameters

  • ~rknn_model_function (string, default: 'yolov8_func'): The name of the RKNN model post-process function file.
  • ~rknn_model_path (string, default: '/home/xiaoqiang/Documents/ros/src/ros_rknn_yolo/model/yolov8s.rknn'): The path to the RKNN model file.
  • ~enable_streaming_detect (bool, default: False): If True, the node will continuously process images from the raw_img_topic_name topic.
  • ~enable_crop (bool, default: False): If True, the objection detection result would contain it's crop image.
  • ~enable_draw (bool, default: False): If True, the node will publish images with the detected objects highlighted to the yolo_output_img_msg topic.
  • ~drop_interval (int, default: 1): The interval at which frames are dropped to control the processing speed.
  • ~obj_thresh (float, default: 0.25): The confidence threshold for object detection.
  • ~nms_thresh (float, default: 0.45): The threshold for non-maximum suppression in object detection.
  • ~classes (list of strings): The list of object classes that the model can detect.
  • ~tpes (int, default: 1): The number of thread pool executors for parallel processing.
  • ~npu_start_id (int, default: 0): The start ID for the NPU (Neural Processing Unit).
  • ~hide_label (bool, default: False): If True, the node will not publish images with the detected objects names.

Dynamic Reconfigure Parameters

  • enable_crop (bool): Enables or disables objection detection result image cropping.
  • enable_draw (bool): Enables or disables the highlighting of detected objects in the published images.
  • enable_streaming_detect (bool): Enables or disables continuous image processing.
  • drop_interval (int): Sets the interval at which frames are dropped to control the processing speed.
  • raw_img_topic_name (string): Sets the name of the image topic to subscribe to.

Install Instructions

Git clone and then catkin_make, the dependent vision_msgs package is here: http://git.bwbot.org/publish/vision_msgs.git

Post-catkin-make-Compilation Instructions

After compiling with catkin, you need to execute sudo ./fix_rknn2_runtime to update rknn2_runtime, so as to ensure the consistency of the npu library function version.

Launch Instructions

The xiaoqiang_yolo.launch is a reference file, which launches two rknn_yolo_node nodes, named yolo_node_1 and yolo_node_2. Both nodes use the rknn_yolo_node.sh script from the ros_rknn_yolo package.

Please note that the rknn_yolo_node.sh script is run within a Python virtual environment. This is because our rknn_yolo_node nodes depend on specific versions of Python libraries, which are installed in the virtual environment. Therefore, when we launch the yolo_node_1 and yolo_node_2 nodes, we are actually launching the rknn_yolo_node.sh script within the Python virtual environment, not the rknn_yolo_node directly.

About The Launch File

Each node loads a default parameter configuration file upon launch, located in the config directory of the ros_rknn_yolo package. For yolo_node_1, the default configuration file is default.yaml, and for yolo_node_2, it is default2.yaml.

If the use_custom_setting argument is set to true, each node also loads an additional parameter configuration file. For yolo_node_1, this file is do_yolo_1.yaml, and for yolo_node_2, it is do_yolo_2.yaml. These files are located in the params directory of the startup package.

Topic Remapping

Each node publishes two topics: yolo_output_msg and yolo_output_img_msg. The names of these topics are remapped in the launch file. For yolo_node_1, yolo_output_msg is remapped to /yolo_node_1/yolo_result, and yolo_output_img_msg is remapped to /yolo_node_1/yolo_result_img. For yolo_node_2, yolo_output_msg is remapped to /yolo_node_2/yolo_result, and yolo_output_img_msg is remapped to /yolo_node_2/yolo_result_img.

Running the Launch File

To run this launch file, you can enter the following command in the terminal:

roslaunch ros_rknn_yolo xiaoqiang_yolo.launch

If you want to use custom parameter configurations, you can add use_custom_setting:=true when running the command:

roslaunch ros_rknn_yolo xiaoqiang_yolo.launch use_custom_setting:=true

Running Basic Test

To run the basic test, follow these steps:

  1. Navigate to the test directory of the ros_rknn_yolo package:
roscd ros_rknn_yolo/test
  1. Run the bus_do_yolo_srv.py script. This script sends a service request to the ~do_yolo service, which triggers the YOLO object detection process:
python bus_do_yolo_srv.py
  1. Observe the output. The script will print the detection results to the console. Each result includes the class and bounding box of the detected object.

Please note that the bus_do_yolo_srv.py script requires a running instance of the rknn_yolo_node. Make sure to launch the node before running the test:

roslaunch ros_rknn_yolo xiaoqiang_yolo.launch