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All Projects → derenlei → Unity_Detection2AR

derenlei / Unity_Detection2AR

Licence: MIT license
Localize 2D image object detection in 3D Scene with Yolo in Unity Barracuda and ARFoundation.

Programming Languages

C#
18002 projects

Labels

unityaugmented-realityunity3dobject-detectionyolov2barracudaonnxyolov3object-localizationarfoundation

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Unity_Detection2AR

A simple solution to incorporate object localization into conventional computer vision object detection algorithms.

IDEA: There aren't that many open source real-time 3D object detection. This is an example of using "more popular" 2D object detection and then localize it with a few feature points. It uses recently released Barracuda for object detection and ARFoundation for AR. It works both on iOS and Android devices.

Currently supports tiny Yolo2 and 3.

demo

Requirements

"com.unity.barracuda": "1.0.3",
"com.unity.xr.arfoundation": "4.0.8",
"com.unity.xr.arkit": "4.0.8",
"com.unity.xr.arcore": "4.0.8"

Usage

It is developed in Unity 2020.2.1 and requires product ready Barracuda with updated AR packages. The preview Barracuda versions seems unstable and may not work.

  • Open the project in Unity (Versions > 2019.4.9).
  • In Edit -> Player Settings -> Other XR Plug-in Management, make sure Initialize XR on Startup and Plug-in providers are marked to enable ARCamera.\
  • From Inspector Scene: Detect -> Game Object: Camera Image -> Script: Phone AR Camera, choose Selected_detector to either Yolo2_tiny or Yolo3_tiny(default).
  • Make sure that Detector has ONNX Model file and Labels file set.
  • For Android, check the Minimum API Level at Project Settings -> Player -> Others Settings -> Minimum API Level. it requires at least Android 7.0 'Nougat' (API Level 24).
  • For Android, also enable Auto Graphics API. See Issue
  • In File -> Build settings choose Detect and hit Build and run.
  • For IOS, fix team setting in Signing & Capabilities.

Detection Model

We currently support Yolo version 2 (tiny) and Yolo version 3 (tiny). Example models are in Assets/Models/.

yolov3-tiny-416.onnx is trained on COCO dataset.

yolov2-tiny-food-freeze.onnx is trained on FOOD100 dataset through darknet. A good example of the training tool is here. Ideally, it can detect 100 categories of dishes.

Image

Use Your Own Model

  1. Convert your model into the ONNX format. If it is trained through Darknet, convert it into frozen tensorflow model first, then ONNX.
  2. Upload the model and label to Assets/Models. Use inspector to update your model settings in Scene: Detect -> Game Object: Detector Yolo2-tiny / Detector Yolo3-tiny. Update anchor info in the DetectorYolo script here or here.

Acknowledgement

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].