  Deep Learning has become exceptionally popular in the last few years due to
its success in computer vision and other fields of AI. However, deep neural
networks are computationally expensive, which limits their application in low
power embedded systems, such as mobile robots. In this paper, an efficient
neural network architecture is proposed for the problem of detecting relevant
objects in robot soccer environments. The ROBO model's increase in efficiency
is achieved by exploiting the peculiarities of the environment. Compared to the
state-of-the-art Tiny YOLO model, the proposed network provides approximately
35 times decrease in run time, while achieving superior average precision,
although at the cost of slightly worse localization accuracy.
