  Object detection models based on convolutional neural networks (CNNs)
demonstrate impressive performance when trained on large-scale labeled
datasets. While a generic object detector trained on such a dataset performs
adequately in applications where the input data is similar to user photographs,
the detector performs poorly on small objects, particularly ones with limited
training data or imaged from uncommon viewpoints. Also, a specific room will
have many objects that are missed by standard object detectors, frustrating a
robot that continually operates in the same indoor environment.
  This paper describes a system for rapidly creating customized object
detectors. Data is collected from a quadcopter that is teleoperated with an
interactive interface. Once an object is selected, the quadcopter autonomously
photographs the object from multiple viewpoints to %create training data that
is used by DUNet (Dense Upscaled Net), collect data to train DUNet (Dense
Upscaled Network), our proposed model for learning customized object detectors
from scratch given limited data. Our experiments compare the performance of
learning models from scratch with DUNet vs.\ fine tuning existing state of the
art object detectors, both on our indoor robotics domain and on standard
datasets.
