  This paper proposes an enhancement of convolutional neural networks for
object detection in resource-constrained robotics through a geometric input
transformation called Visual Mesh. It uses object geometry to create a graph in
vision space, reducing computational complexity by normalizing the pixel and
feature density of objects. The experiments compare the Visual Mesh with
several other fast convolutional neural networks. The results demonstrate
execution times sixteen times quicker than the fastest competitor tested, while
achieving outstanding accuracy.
