DoorDetect Dataset

DoorDetect is a dataset of 1,02 images that have been annotated with doors Total 174 doors..

Images

The images annotated are from Open Images Dataset V4 and MCIndoor20000 .

Labels

The object location is specified by the coordinates of its bounding box. Boxes were marked using 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.

annotation file of the form
"[[xmin1, ymin1, width1, height1, idx1], ]" (was converted with convert_annotations.py)

tnesorflow faster rcnn see github eyalho/faster_rcnn

YOLO with DoorDetect

The dataset can be used for training and testing an object detection CNN such as YOLO. Weights for detecting doors and handles with YOLO can be downloaded from: YOLO_weights (mAP=45%). For running YOLO you might also need the network configuration file yolo-obj.cfg and a text file where the detected classes names and their order is specified obj.names.

Citation

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

  @misc{arduengo2019robust,
      title={Robust and Adaptive Door Operation with a Mobile Manipulator Robot},
      author={Miguel Arduengo and Carme Torras and Luis Sentis},
      year={2019},
      eprint={1902.09051},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
    }