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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Object detection for waste sorting

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Rumfels, Océane ULiège
Promotor(s) : Louppe, Gilles ULiège ; Rebbouh, Leila
Date of defense : 24-Jun-2021/25-Jun-2021 • Permalink : http://hdl.handle.net/2268.2/11486
Details
Title : Object detection for waste sorting
Author : Rumfels, Océane ULiège
Date of defense  : 24-Jun-2021/25-Jun-2021
Advisor(s) : Louppe, Gilles ULiège
Rebbouh, Leila 
Committee's member(s) : Boigelot, Bernard ULiège
Sacré, Pierre ULiège
Language : English
Number of pages : 65
Keywords : [en] object detection
[en] waste sorting
[en] waste classification
[en] detection benchmark study for waste sorting
[en] classification
[en] YOLO
[en] ResNet
[en] RetinaNet
[en] Faster R-CNN
Discipline(s) : Engineering, computing & technology > Computer science
Target public : Researchers
Professionals of domain
Student
Complementary URL : https://github.com/oceanerumfels/Object-detection-for-waste-sorting
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] As household waste increases, it becomes more and more important to sort and recycle. However European studies suggest that there is still an important part of incorrect sorting, and that many citizens could use an assistance of some sort. In the last few years, computer vision algorithms have been used to tackle this problem with high classification accuracies. However, fewer works required a sorting with more than six classes, as some features might become too difficult to distinguish. This thesis presents a comparative study of several object detection algorithms, for sorting of trash pieces following IDELUX's sorting directives and real-time constraints. IDELUX's dataset of 10050 waste images is divided into 12 classes. Some of those classes are very similar and with an important imbalance in the number of samples per class, this dataset presents a new challenge in the waste detection field. Four algorithms were specifically trained and tested in order to determine which architecture was the fittest to perform the task, whether it could be performed by a simple classifier, a one stage or a two stage detector. The proposed objects detectors are RetinaNet, YOLOv5 and Faster R-CNN, while the chosen classifier is a ResNet model. The models were evaluated on their accuracy, their mean average precision, their IOU, their inference time and their training time. For this specific project, ResNet outperforms all of the other models and achieves an accuracy of 90 \%. Overall, the results show that classification and detection algorithms are capable of tackling more complex waste sorting problems than the ones currently explored in the literature.


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Author

  • Rumfels, Océane ULiège Université de Liège > Master ingé. civ. info., à fin.

Promotor(s)

Committee's member(s)

  • Boigelot, Bernard ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Informatique
    ORBi View his publications on ORBi
  • Sacré, Pierre ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Robotique intelligente
    ORBi View his publications on ORBi
  • Total number of views 88
  • Total number of downloads 11










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