基于 YOLOv3 和 PyTorch，使用 ROS 进行实时物体检测.zip

# yolov3_pytorch_ros This package provides a ROS wrapper for [YOLOv3](https://pjreddie.com/darknet/yolo) based on [PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3). For consistency, the [messages](msg) are based on the [darknet_ros](https://github.com/leggedrobotics/darknet_ros) package. The package has been tested with Ubuntu 16.04 and ROS Kinetic on a NVIDIA Jetson TX2. **Authors**: Vasileios Vasilopoulos (<vvasilo@seas.upenn.edu>), Georgios Pavlakos (<pavlakos@seas.upenn.edu>) **Affiliation**: [University of Pennsylvania, GRASP Lab](https://www.grasp.upenn.edu) ## Prerequisites To download the prerequisites for this package (except for ROS itself), use the [ROS-default `rosdep` to install the required dependencies](http://wiki.ros.org/ROS/Tutorials/rosdep): ``` cd /your/catkin_ws rosdep install --from-paths src --ignore-src -y ``` Alternatively you can use the python standard `requirements.txt` file to install the dependencies as well. Navigate to the package folder and run: ``` $ sudo pip install -r requirements.txt ``` ## Installation Aftre making sure the required dependencies are installed following the instructions above, navigate to your catkin workspace and run: ``` $ catkin_make --pkg yolov3_pytorch_ros ``` ## Basic Usage 1. First, make sure to put your weights in the [models](models) folder. This should automatically be done for the default configuration during the compilation process, however if you'd like to use your own make sure that they exist before you use them. For the **training process** in order to use custom objects, please refer to the original [YOLO page](https://pjreddie.com/darknet/yolo/). By default during the build the following pre-trained weights are downloaded: ``` # download yolov3.weights wget http://pjreddie.com/media/files/yolov3.weights # dowload yolov3-tiny.weights wget http://pjreddie.com/media/files/yolov3-tiny.weights ``` 2. The default settings (using `yolov3.weights`) in the `launch/detector.launch` file should work, all you should have to do is change the image topic you would like to subscribe to: ``` roslaunch yolov3_pytorch_ros detector.launch image_topic:=/your/image/topic ``` You can also try out the `yolov3-tiny.weights` by simply passing in different arguments at launch. This is the recommended usage on a CPU for dramatically increased framerate with slightly reduced performance: ``` roslaunch yolov3_pytorch_ros detector.launch image_topic:=/your/image/topic config_name:=yolov3-tiny.cfg weights_name:=yolov3-tiny.weights confidence:=0.1 ``` The `confidence` argument can be adjusted to set a threshold for detected objects to be ignored. Alternatively you can modify the parameters in the [launch file](launch/detector.launch), recompile and launch it that way so that no arguments need to be passed at runtime. ### Node parameters * **`image_topic`** (string) Subscribed camera topic. * **`weights_name`** (string) Weights to be used from the [models](models) folder. * **`config_name`** (string) The name of the configuration file in the [config](config) folder. Use `yolov3.cfg` for YOLOv3, `yolov3-tiny.cfg` for tiny YOLOv3, and `yolov3-voc.cfg` for YOLOv3-VOC. * **`classes_name`** (string) The name of the file for the detected classes in the [classes](classes) folder. Use `coco.names` for COCO, and `voc.names` for VOC. * **`publish_image`** (bool) Set to true to get the camera image along with the detected bounding boxes, or false otherwise. * **`detected_objects_topic`** (string) Published topic with the detected bounding boxes. * **`detections_image_topic`** (string) Published topic with the detected bounding boxes on top of the image. * **`confidence`** (float) Confidence threshold for detected objects. ### Subscribed topics * **`image_topic`** (sensor_msgs::Image) Subscribed camera topic. ### Published topics * **`detected_objects_topic`** (yolov3_pytorch_ros::BoundingBoxes) Published topic with the detected bounding boxes. * **`detections_image_topic`** (sensor_msgs::Image) Published topic with the detected bounding boxes on top of the image (only published if `publish_image` is set to true). ## Citing The YOLO methods used in this software are described in the paper: [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640). If you are using this package, please add the following citation to your publication: @misc{vasilopoulos_pavlakos_yolov3ros_2019, author = {Vasileios Vasilopoulos and Georgios Pavlakos}, title = {{yolov3_pytorch_ros}: Object Detection for {ROS} using {PyTorch}}, howpublished = {\url{https://github.com/vvasilo/yolov3_pytorch_ros}}, year = {2019}, }