vision_darknet_detect's Introduction

Vision Darknet Detect

master	develop

Autoware package based on Darknet that supports Yolov3 and Yolov2 image detector.

Requirements

NVIDIA GPU with CUDA installed
Pretrained YOLOv3 or YOLOv2 model on COCO dataset, Models found on the YOLO website.
The weights file must be placed in vision_darknet_detect/darknet/data/.

How to launch

From a sourced terminal:
- roslaunch vision_darknet_detect vision_yolo3_detect.launch
- roslaunch vision_darknet_detect vision_yolo2_detect.launch
From Runtime Manager:

Computing Tab -> Detection/ vision_detector -> vision_darknet_detect You can change the config and weights file, as well as other parameters, by clicking [app]

Parameters

Launch file available parameters:

Parameter	Type	Description
`score_threshold`	Double	Detections with a confidence value larger than this value will be displayed. Default `0.5`.
`nms_threshold`	Double	Non-Maximum suppresion area threshold ratio to merge proposals. Default `0.45`.
`network_definition_file`	String	Network architecture definition configuration file. Default `yolov3.cfg`.
`pretrained_model_file`	String	Path to pretrained model. Default `yolov3.weights`.
`camera_id`	String	Camera workspace. Default `/`.
`image_src`	String	Image source topic. Default `/image_raw`.
`names_file`	String	Path to pretrained model. Default `coco.names`.

Subscribed topics

Topic	Type	Objective
`/image_raw`	`sensor_msgs/Image`	Source image stream to perform detection.
`/config/Yolo3`	`autoware_config_msgs/ConfigSSD`	Configuration adjustment for threshold.

Published topics

Topic	Type	Objective
`/detection/vision_objects`	`autoware_msgs::DetectedObjectArray`	Contains the coordinates of the bounding box in image coordinates for detected objects.