Abstract
In this paper we present our research on the optimisation of a deep
neural network for 3D object detection in a point cloud. Techniques like
quantisation and pruning available in the Brevitas and PyTorch tools
were used. We performed the experiments for the PointPillars network,
which offers a reasonable compromise between detection accuracy and
calculation complexity. The aim of this work was to propose a variant of
the network which we will ultimately implement in an FPGA device. This
will allow for real-time LiDAR data processing with low energy
consumption. The obtained results indicate that even a significant
quantisation from 32-bit floating point to 2-bit integer in the main
part of the algorithm, results in 5%-9% decrease of the detection
accuracy, while allowing for almost a 16-fold reduction in size of the
model.