Abstract
In this paper, we present a hardware-software implementation of a deep neural network for object detection based on a point cloud obtained by a LiDAR sensor. The Brevitas / PyTorch tools were used for network quantisation and the FINN tool for hardware implementation in the reprogrammable Zynq UltraScale+ MPSoC device. The PointPillars network was used in the research, as it is a reasonable compromise between detection accuracy and calculation complexity. The obtained results show that quite a significant computation precision limitation along with a few network architecture simplifications allows the solution to be implemented on an heterogeneous embedded platform with reasonable detection accuracy.
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