# DoorDetect-Dataset

**Repository Path**: gtengfei/DoorDetect-Dataset

## Basic Information

- **Project Name**: DoorDetect-Dataset
- **Description**: door data数据集
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-09-14
- **Last Updated**: 2022-09-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DoorDetect Dataset
DoorDetect is a dataset of 1,213 images that have been annotated with object bounding boxes. The images are very diverse and often contain complex scenes with several objects. 


## Images
The images annotated are from [Open Images Dataset V4](https://storage.googleapis.com/openimages/web/index.html) and [MCIndoor20000 ](https://github.com/bircatmcri/MCIndoor20000).

![alt text](/readme_figures/Fig1.png)


## Object Classes 
The identified object classes are: ***handle***; ***door***, which refers to any room door; ***cabinet door***, which refers to any drawer or small door; and ***refrigerator door***, which refers to any door in a refrigerator.  

![alt text](/readme_figures/Fig2.png)


## Labels
The object location is specified by the coordinates of its bounding box. Boxes were marked using [Yolo_mark](https://github.com/AlexeyAB/Yolo_mark). There is a .txt file for each image with the same name. Each line in the label file is of the form: `<object-class> <x> <y> <width> <height>`.

Where:
* `<object-class>`: integer number of object. (0) *door*; (1) *handle*; (2) *cabinet door*; (3) *refrigerator door*.
* `<x> <y> <width> <height>`: float values relative to width and height of the image.
* `<x> <y>`: center of the box.

![alt text](/readme_figures/Fig3.png)


## YOLO with DoorDetect
The dataset can be used for training and testing an object detection CNN such as [YOLO](https://pjreddie.com/darknet/yolo/). Weights for detecting doors and handles with YOLO can be downloaded from: [YOLO_weights](https://drive.google.com/open?id=1i9E9pTPN5MtRxgBJWLnfQl2ypCv92dXk) (mAP=45%). For running YOLO you might also need the network configuration file [yolo-obj.cfg](https://github.com/MiguelARD/DoorDetect-Dataset/blob/master/yolo-obj.cfg) and a text file where the detected classes names and their order is specified [obj.names](https://github.com/MiguelARD/DoorDetect-Dataset/blob/master/obj.names).

![alt text](/readme_figures/Fig4.png)

## Citation
Please cite the paper in your publications if it helps your research.

```
@article{Arduengo_2021,
   title={Robust and adaptive door operation with a mobile robot},
   ISSN={1861-2784},
   url={http://dx.doi.org/10.1007/s11370-021-00366-7},
   DOI={10.1007/s11370-021-00366-7},
   journal={Intelligent Service Robotics},
   publisher={Springer Science and Business Media LLC},
   author={Arduengo, Miguel and Torras, Carme and Sentis, Luis},
   year={2021},
   month={May}
}

```

Link to the paper: [Robust and Adaptive Door Operation with a Mobile Robot](https://link.springer.com/article/10.1007/s11370-021-00366-7)