  Mobile autonomous robots include numerous sensors for environment perception.
Cameras are an essential tool for robot's localization, navigation, and
obstacle avoidance. To process a large flow of data from the sensors, it is
necessary to optimize algorithms, or to utilize substantial computational
power. In our work, we propose an algorithm for optimizing a neural network for
object detection using preliminary binary frame classification. An autonomous
outdoor mobile robot with 6 rolling-shutter cameras on the perimeter providing
a 360-degree field of view was used as the experimental setup. The obtained
experimental results revealed that the proposed optimization accelerates the
inference time of the neural network in the cases with up to 5 out of 6 cameras
containing target objects.
