Faculté des Sciences appliquées
Faculté des Sciences appliquées

Segmentation multi-capteurs de déchets métalliques pour tri robotisé

Raimondi, Rémi ULiège
Promotor(s) : Louppe, Gilles ULiège
Date of defense : 24-Jun-2021/25-Jun-2021 • Permalink :
Title : Segmentation multi-capteurs de déchets métalliques pour tri robotisé
Translated title : [en] Multi-sensor instance segmentation of scraps for robotic sorting
Author : Raimondi, Rémi ULiège
Date of defense  : 24-Jun-2021/25-Jun-2021
Advisor(s) : Louppe, Gilles ULiège
Committee's member(s) : Barnabé, Pierre ULiège
Geurts, Pierre ULiège
Dislaire, Godefroid ULiège
Language : English
Number of pages : 80
Keywords : [en] deep learning
[en] instance segmentation
[en] robotic
[en] sorting
Discipline(s) : Engineering, computing & technology > Computer science
Name of the research project : Multipick
Target public : Researchers
Professionals of domain
General public
Complementary URL :
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil électricien, à finalité spécialisée en "signal processing and intelligent robotics"
Faculty: Master thesis of the Faculté des Sciences appliquées


[en] During the last decades, the intensive exploitation of natural resources has known a significant growth due to the increasing need for raw materials. As a consequence, the environment has been subjected to a lot of damage. Modestly, by improving recycling and waste recovery, this master's thesis contributes to a partial solution that could slow down the exploitation of natural resources. Indeed, this thesis is carried out as part of the Multipick project which is aiming at creating an industrial demonstrator capable of sorting over 20,000 tonnes of metals per year at a rate of 16 scraps per second thanks to the use of robots and artificial intelligence. More precisely, the objective of this master's thesis is to study and assess the potential of deep neural networks to increase the performance of the actual waste characterization system operating inside a prototype version of the demonstrator called Pick-it. The innovation of this work lies in the type of input processed by the deep neural network: instead of using classical RGB images, the deep neural network is fed with multi-feature tensors composed of eleven 2D images staked together and containing diverse characteristics extracted from a dual X-Ray transmission sensor, a 3D ranging camera, and a hyperspectral camera. Leveraging these multi-feature tensors, a fully functional and optimized Mask R-CNN deep neural network is successfully integrated inside the pioneer Pick-it prototype. This model has been shown to achieve excellent results in terms of localization but reaches too low classification accuracy with the prototype to outperform the current waste characterization method. Nevertheless, the achieved results show great potential and unveil various directions of future work that could lead to the creation of a deep learning model outperforming the current characterization method and reaching state-of-the-art performance in the task of multi-sensor instance segmentation.



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  • Raimondi, Rémi ULiège Université de Liège > Master ingé. civ. électr., à fin.


Committee's member(s)

  • Barnabé, Pierre ULiège Université de Liège - ULiège > Département ArGEnCo > Géoressources minérales & Imagerie géologique
    ORBi View his publications on ORBi
  • Geurts, Pierre ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Dislaire, Godefroid ULiège Université de Liège - ULiège > Département ArGEnCo > Géoressources minérales & Imagerie géologique
    ORBi View his publications on ORBi
  • Total number of views 78
  • Total number of downloads 297

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