2021 • In 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26–29, 2021, Proceedings, Part II
[en] The rising quality and throughput demands of the manufacturing domain require flexible, accurate and explainable computer-vision solutions for defect detection. Deep Neural Networks (DNNs) reach state-of-the-art performance on various computer-vision tasks but wide-spread application in the industrial domain is blocked by the lacking explainability of DNN decisions. A promising, human-readable solution is given by saliency maps, heatmaps highlighting the image areas that influence the classifier’s decision. This work evaluates a selection of saliency methods in the area of industrial quality assurance. To this end we propose the distance pointing game, a new metric to quantify the meaningfulness of saliency maps for defect detection. We provide steps to prepare a publicly available dataset on defective steel plates for the proposed metric. Additionally, the computational complexity is investigated to determine which methods could be integrated on industrial edge devices. Our results show that DeepLift, GradCAM and GradCAM++ outperform the alternatives while the computational cost is feasible for real time applications even on edge devices. This indicates that the respective methods could be used as an additional, autonomous post-classification step to explain decisions taken by intelligent quality assurance systems.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
LORENTZ, Joe ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Hartmann, Thomas; DataThings S.A.
Moawad, Assaad; DataThings S.A.
Fouquet, Francois; DataThings S.A.
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Explaining Defect Detection with Saliency Maps
Date de publication/diffusion :
19 juillet 2021
Nom de la manifestation :
34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Date de la manifestation :
from 26.07.2021 to 29.07.2021
Titre de l'ouvrage principal :
34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26–29, 2021, Proceedings, Part II
Maison d'édition :
Springer, Cham, Suisse
ISBN/EAN :
978-3-030-79463-7
Pagination :
506-518
Peer reviewed :
Peer reviewed
Projet FnR :
FNR14297122 - Towards Edge-optimized Deep Learning For Explainable Quality Control, 2019 (01/01/2020-31/12/2023) - Joe Lorentz
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