Optimisation of the PointPillars network for 3D object detection in point clouds

Autor:	Stanisz, Joanna, Lis, Konrad, Kryjak, Tomasz, Gorgon, Marek
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
DOI:	10.23919/SPA50552.2020.9241265
Popis:	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. Comment: 7 pages, 2 figures, submitted to SPA 2020 conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.00493 Zobrazit plný text záznamu View this record from Arxiv