Szczegóły publikacji
Opis bibliograficzny
Implementation of the PointPillars network for 3D object detection in reprogrammable heterogeneous devices using FINN / Joanna STANISZ, Konrad LIS, Marek GORGOŃ // Journal of Signal Processing Systems for Signal, Image, and Video Technology ; ISSN 1939-8018 . — Tytuł poprz.: Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology ; ISSN: 1387-5485. — 2022 — vol. 94 iss. 7, s. 659–674. — Bibliogr. s. 673–674, Abstr. — Publikacja dostępna online od: 2021-12-27. — 14th Workshop on design and architectures for signal and image processing : held in conjunction with the 16th HiPEAC conference : Budapest, Hungary, January 18–20, 2021
Autorzy (3)
Dane bibliometryczne
| ID BaDAP | 141196 |
|---|---|
| Data dodania do BaDAP | 2022-07-18 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s11265-021-01733-4 |
| Rok publikacji | 2022 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Signal Processing Systems for Signal, Image, and Video Technology |
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 PointPillars network was used in the research, as it is a reasonable compromise between detection accuracy and calculation complexity. The Brevitas / PyTorch tools were used for network quantisation (described in our previous paper) and the FINN tool for hardware implementation in the reprogrammable Zynq UltraScale+ MPSoC device. 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 a heterogeneous embedded platform with maximum 19% AP loss in 3D, maximum 8% AP loss in BEV and execution time 375ms (the FPGA part takes 262ms). We have also compared our solution in terms of inference speed with a Vitis AI implementation proposed by Xilinx (19 Hz frame rate). Especially, we have thoroughly investigated the fundamental causes of differences in the frame rate of both solutions. The code is available at https://github.com/vision-agh/pp-finn.
