Survey on Object Detection Using Deep Reinforcement Learning

Abstract

Deep Reinforcement Learning (DRL) is a
method that is a combination of
Reinforcement Learning framework and deep
neural networks. It is observed that DRL
achieved a remarkable victory over the fields
such as video games, robotics, finance,
computer vision, health care etc. Comparing
other domains, the medicine and healthcare
field has benefitted a lot from DRL. In this
paper, we study the role of DRL in object
detection using the works of various authors.
Here we focus on object detection in medicine
and the healthcare field. It is observed that
the authors experience higher speed in the
DRL algorithm compared to classic methods.
The respective methods are more efficient
and accurate working on CT/MRI images.
Most authors use an updated DRL algorithm
in the stage of feature extraction and also club
it with some machine learning techniques.
DQN (Deep Q Network), Double DQN,
TRPO(Trust Region Policy Optimization) etc
are some common DRL algorithms used by
researchers. This literature survey
emphasizes methodologies of application of
DRL algorithms for more efficient object
detection. This review helps the futuristic way
to develop a DRL algorithm for better object
detection in the healthcare domain and
similar ones.