# Sujets 6.1 : Smiths group

# Introduction

Presentation psr Clement Fang
- Ancien Image 2020

Serge Maitrejean
- Responsable de l'innovation
- Doctorat en physique

Eric Garrido
- Doctorat en physique nucleaire

Groupe Smiths
- Cote en bourse a Londres
- 4 divisions
- Smith detection: scanner et detection
- Detection de choses illicites ou non-declarees


En france:
- Base a Vitry-sur-Seine
- A peu pres 200 personnes
- IA / traitement d'image...


# Les techs au sein de Smith

# Global presence

# Vehicle, cargo & mobile screening

# High energy X-ray imaging

## Which target / threat we are looking for ?

## SD Paris Partnerships
> Epita est cense etre la

# Cigarettes detection

## Some big seizures made thanks to our iCmore in the news

## iCmore Weapons detection

# More in-depth
## Truck Radioscopy
- Imaging with X-Rays but with a scanning principle


- Pulsed X-ray source: X-Ray pulses (flash) oh three $\mus$ every 2 or 3 milliseconds
- One vertical line of detectors/pixels (5 or 20 mm width, 5 mm height): one column of image is recorded for each X-ray pulses
- Truck speed is limited: < displacement of detector width between 2 pulses (typically 5-7 km/h)
- Or resolution is bad (large detectors)

## A new tech: Matrix detector
- Multicolumn detector (column)


- Large resolution improvement (Left one line, Right Matrix detector)

## But noting or nobody is perfect
- First problem: missing part 
- Easy to solve by slowing down the speed but... 
- Superposition: le meme point se voit 2 fois

## The problem of depth at low speed: rearranging data ?
- The way of ordering data is depending on the depth where objects are located.. But we don't know the depth ! It's a stereo effect


## Ordering data is depending on the depth
- We have to assume where in depth the object are located, if we are wrong strong artifacts appears


## Turning a drawback onto an advantage
- Minimizing the artifacts $\Leftrightarrow$ Finding the depth of the objects and providing a optimum high resolution image

## Curent status
- Proof of concept has been done using energy minimization technics
- Work on this approach is pursuing
- A comprehensive set of data has been acquired from which the "exact images" can be extracted
- We want to test another approach, neural networks and deep learning are good candidates

## The work
- Getting familiarized with the problem (not so easy)
- Getting familiarized with the current method
- Initating Matrix Detector Deep Learning process for:
- Building the best radioscopic planar images
- Finding the depth where objects are located