Post-doc in Computer Vision for Soil
il y a 1 semaine
Post-doc in Computer Vision for
soil characterization in tunnel construction
Context:
In recent years, the issue of resource efficiency has also become increasingly important in con-struction engineering, as soil and rock account for more than 50% of mineral construction waste.Tunnel projects play a special role in this regard, as large quantities are generated at specific timesand places. Due to the high degree of mechanisation and the associated advantages in terms of
This 24-month post-doc position is funded by the French-German ANR project REMATCH.
The overall objective of this project is to use the data sets from TBMs, with the help of AI methods,to enhance the recycling of the large quantities of tunnel excavation material. In this regard, aninnovative real-time measurement system for material characterisation is to be developed whichgives decision support on the question if soil is “usable” or “not usable” for other purposes and thushas to be disposed of in a landfill. This system will base on several approaches using AI methods: firstly, on image recognition of excavated material, secondly, on intelligent data processing of TBMdata, and, thirdly, on a knowledge database.
Objectives:
The objectives of this post-doc position are to develop a computer vision system that analyses thevideos captured from one or several cameras installed at the TBM and filming the excavatedmaterial on the conveyor belt. In order to decide on the possible reuse of the material, differentgeophysical properties needs to be estimated from visual features extracted in real-time from thevideo stream(s) coming from RGB cameras. This is challenging due to the mediocre acquisition
conditions under low lighting and fast motion inducing some motion blur. More specifically, aftersome preprocessing, a first step is to develop a machine learning solution based on appropriate CNN models that are trained for either classification and/or regression tasks in a supervised manner.
Different models should be designed, implemented and evaluated in terms of robustness and precision.
To go further, novel innovative neural network-based architectures and weakly supervised learning schemes should be proposed to learn a latent representation that reflects the meaningful similaritiesof relevant soil characteristics. Then, potentially other physical properties should be incorporated
more explicitely into this semantic latent representation (either via a specific CNN or autoencoder-type model) to make the learnt features and models more explainable. After evalating these models,they should be appropriately integrated with other AI models that are not based on images but on TBM sensor data.
Environment:
Type d'emploi : Temps plein
Exigences linguistiques flexibles:
- Français non requis
Programmation:
- Périodes de travail de 8 heures
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