ROS YOLOv2 integration

Integrates YOLOv2 with ROS, generating messages with position and confidence of detection.

Requirements

ROS (tested on Jade and Kinetic)
GPU supporting CUDA

Installation

If using wstool, the following .rosinstall entry adds this repository to your workspace:

- git:
    local-name: yolo2
    uri: https://github.com/ThundeRatz/ros_yolo2.git

If building from source, change to the src directory of your workspace and use git clone --recursive https://github.com/tiagoshibata/ros_yolo2.git to download with submodules.

This repository is using ROS's nodelet API, which allows loading multiple nodelets in the same process, supporting zero-copy message transfers between nodelets. No node executables are built; instead, shared libraries are. To use them, check the nodelet docs and tutorials. This sample launchfile creates a nodelet manager with an uvc_camera and a yolo2 nodelet:

<launch>
  <group ns="vision">
    <node pkg="nodelet" type="nodelet" name="vision_nodelet" args="manager"/>
    <node pkg="nodelet" type="nodelet" name="camera" args="load uvc_camera/CameraNodelet vision_nodelet" />
    <remap from="yolo2/image" to="image_raw" />
    <node pkg="nodelet" type="nodelet" name="yolo2" args="load yolo2/Yolo2Nodelet vision_nodelet">
      <param name="confidence" value="0.5" />
      <param name="nms" value="0.4" />
    </node>
  </group>
</launch>

Setup

Copy your network config to data/yolo.cfg and network weights as data/yolo.weights.

Message format

For every image received, an ImageDetections message is generated at the yolo2_detections topic, containing a Header and a vector of Detection. The header time stamp is copied from the received image message; this way, a set of detections can be matched to a specific image. Each Detection contains the following attributes:

uint32 class_id - Class number configured during training;
float32 confidence - Confidence;
float32 x - X position of the object's center relative to the image (between 0 = left and 1 = right);
float32 y - Y position of the object's center relative to the image (between 0 = top and 1 = bottom);
float32 width - Width of the object relative to the image (between 0 and 1);
float32 height - Height of the object relative to the image (between 0 and 1).

Contributors