# Fast Meteor Detection Toolbox

Mathuran Kandeepan, Clara Ciocan, Maxime Millet, Manuel Bouyer, Adrien

Cassagne, Lionel Lacassagne

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## ► To cite this version:

Mathuran Kandeepan, Clara Ciocan, Maxime Millet, Manuel Bouyer, Adrien Cassagne, et al. Fast Meteor Detection Toolbox. Workshop AFF3CT, Nov 2022, Bordeaux, France., 10.13140/RG.2.2.12222.36161. hal-03954992

HAL Id: hal-03954992

https://hal.science/hal-03954992

Submitted on 26 Jan 2023

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# Fast Meteor Detection Toolbox

MATHURAN KANDEEPAN, CLARA CIOCAN, MAXIME MILLET, MANUEL BOUYER, ADRIEN CASSAGNE AND LIONEL LACASSAGNE

Sorbonne Université, CNRS, LIP6, F-75005, Paris, France

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SORBONNE
UNIVERSITÉ

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## CONTEXT

* **Detection and characterization of meteoroids** and space debris that enter into our atmosphere

- From satellite (CubeSat) [1, 2]

- From weather balloon

- From aircraft observation campaign [3]

<figure><img src="image_6.png" /></figure>

- Better observation conditions than from earth

* Greyscale input videos, output tracks (= positions of meteors)

* **Real-time constraints & Challenges**

- Non-stabilized cameras, noisy video sequences

- Limited computational power: $\lesssim 10$ Watts

- **Embedded systems** (RPi, Jetson, ...) & RISC architectures [4]

* LIP6/ALSOC & IMCCE joint-team: https://www.imcce.fr/recherche/campagnes-observations/meteors/2022the

<figure><img src="image_7.png" /><figcaption>Composite closeup view of the 34 $\tau$-Herculids meteor cluster (duration: 7.5 s) from an airborne observation campaign [3]. Center: *Ursa Minor* constellation.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>## FMDT: COMPUTER-VISION DETECTION CHAIN</figcaption></figure>

Computer-vision detection detailed chain.

<figure><img src="image_9.png" /></figure>

open source
initiative

* Source code available on GitHub: https://github.com/alsoc/fmdt

<figure><img src="image_10.png" /><figcaption>## TASKS GRAPH AND MULTI-THREADING WITH AFF3CT</figcaption></figure>

AFF3CT pipeline parallel construct.

A **3-stage pipeline** using the AFF3CT DSEL [5] has been implemented:

1. Copy image $I_t$ (sequential stage)

2. Costly processing tasks from hysteresis to morphological thresholding (parallel stage)

3. Lightweight processing tasks from $k$-NN to tracking (sequential stage)

* Design choice: most tasks of stage 3 are parallel but they are run on a single thread

- Image processing results from $I_t$ are **memorized** for iteration $t + 1$

- **Dependency**: stage 3 tasks require memorized $t - 1$ statistics

* Tracking is mainly a sequential task, history management of statistics

<figure><img src="image_11.png" /><figcaption>## METEORS AUTOMATIC DETECTION EFFICIENCY</figcaption></figure>

Overlapped duration of ground truth and automatic detection (overall tracking rate: $T_r = 80.4\%$).

## REFERENCES

[1] N. Rambaux et al. Meteorix: a CubeSat mission dedicated to the detection of meteors. In COSPAR, 2018.

[2] M. Millet et al. Meteorix: High performance computer vision app. for Meteor detection from a CubeSat. In COSPAR, 2022.

[3] J. Vaubaillon et al. A 2022 $\tau$-herculid meteor cluster from airborne experiment: automated detection, characterization and consequences for meteoroids. *Astronomy & Astrophysics*, September 2022. (Submitted).

[4] L. Lacassagne and B. Zavidovique. Light Speed Labeling for RISC architectures. In ICIP. IEEE, 2009.

[5] A. Cassagne et al. A DSEL for high throughput and low latency software-defined radio on multicore CPUs. *Wiley Concurrency and Computation: Practice and Experience*, July 2022. (Submitted).

<figure><img src="image_12.png" /><figcaption>## SEQUENTIAL TIME DISTRIBUTION</figcaption></figure>

## EXPERIMENTATION RESULTS

* Video resolution: 1920x1200 (HD)

* GNU compiler: -O3 -march=native

* **Odroid-XU4** – Feb. 2016 – 28 nm proc.

- 4 × LITTLE ARMv7 Cortex-A7 CPU @ 1.4 GHz

- 4 × Big ARMv7 Cortex-A15 CPU @ 1.5 GHz

<table><thead><tr><th></th><th>Conf.†</th><th>FPS</th><th>L (ms)</th><th>E (mJ)</th><th>P (W)</th></tr></thead><tbody><tr><td>Seq.</td><td>LITTLE</td><td>7</td><td>142.6</td><td>403.7</td><td>2.82</td></tr><tr><td>Seq.</td><td>Big</td><td>14</td><td>70.4</td><td>274.6</td><td>3.88</td></tr><tr><td>Pip.</td><td>1L/4L/1L</td><td>26</td><td>361.8</td><td>153.1</td><td>4.04</td></tr><tr><td>Pip.</td><td>1B/4B/1B</td><td>42</td><td>216.1</td><td>159.9</td><td>6.74</td></tr><tr><td>Pip.</td><td>1/8/1</td><td>59</td><td>324.0</td><td>127.5</td><td>7.53</td></tr></tbody></table>

* Nvidia Jetson Nano – Mar. 2019 – 20 nm proc.

- 4 × ARMv8 Cortex-A57 CPU @ 1.479 GHz

<table><thead><tr><th></th><th>Conf.†</th><th>FPS</th><th>L (ms)</th><th>E (mJ)</th><th>P (W)</th></tr></thead><tbody><tr><td>Seq.</td><td>–</td><td>34</td><td>29.0</td><td>114.9</td><td>3.93</td></tr><tr><td>Pip.</td><td>1/2/1</td><td>66</td><td>76.5</td><td>77.8</td><td>5.21</td></tr><tr><td>Pip.</td><td>1/4/1</td><td>91</td><td>98.5</td><td>68.1</td><td>6.25</td></tr></tbody></table>

* **RaspberryPi 4** model B – Jun. 2019 – 28 nm proc.

- 4 × ARMv8 Cortex-A72 CPU @ 1.5 GHz

<table><thead><tr><th></th><th>Conf.†</th><th>FPS</th><th>L (ms)</th><th>E (mJ)</th><th>P (W)</th></tr></thead><tbody><tr><td>Seq.</td><td>–</td><td>20</td><td>48.7</td><td>156.5</td><td>3.19</td></tr><tr><td>Pip.</td><td>1/2/1</td><td>28</td><td>181.9</td><td>130.5</td><td>3.75</td></tr><tr><td>Pip.</td><td>1/4/1</td><td>30</td><td>310.8</td><td>123.4</td><td>3.79</td></tr></tbody></table>

* Apple Silicon M1 Pro – Oct. 2021 – 5 nm proc.

- 2 × E-core ARMv8 Icestorm CPU @ ≈ 2.0 GHz

- 8 × P-core ARMv8 Firestorm CPU @ ≈ 3.0 GHz

<table><thead><tr><th></th><th>Conf.†</th><th>FPS</th><th>L (ms)</th><th>E (mJ)</th><th>P (W)</th></tr></thead><tbody><tr><td>Seq.</td><td>E-core</td><td>60</td><td>16.1</td><td>34.0</td><td>2.04</td></tr><tr><td>Seq.</td><td>P-core</td><td>206</td><td>4.8</td><td>30.4</td><td>6.27</td></tr><tr><td>Pip.</td><td>1E/2E/1E</td><td>118</td><td>42.0</td><td>19.7</td><td>2.33</td></tr><tr><td>Pip.</td><td>1P/2P/1P</td><td>409</td><td>12.4</td><td>25.8</td><td>10.55</td></tr><tr><td>Pip.</td><td>1/8/1</td><td>1280</td><td>14.6</td><td>21.8</td><td>27.98</td></tr></tbody></table>

<footnote>† Pipeline config.: [stage 1, # of threads] / [stage 2, # of threads] / [stage 3, # of threads]</footnote>

<footnote>FPS: frames per sec., $L$: latency per frame, $E$: energy consumption per frame, $P$: instant power</footnote>