Lien de la 1ere note Hackmd Lien de la 2nd note Hackmd
Sujets 6.1
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
Sujets 6.2
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…
High energy discrimination
- Same principle as an X-ray
- It looks like and X-ray
- but with an X-ray we only have grayscale information
Plus un objet et dense et epais, plus il sera noir
Work to do
Improving the performance of the material discrimination project by:
- Better management of the overlay problem
- Creatin better quality of scans
Overlay
- 2 ojects that overlay make the material detection wrong
- Find a method to segment the objects, then assign their atomic number
Improve scan quality
Create a neural network to convert acquisition from low device to high
An AI that assigns the atomic number of objects
Create a color scale image with only the grayscale image
Provided
- Reference method
- Database
- Script to help for you for your tasks