Reference : Explaining Defect Detection with Saliency Maps
E-prints/Working papers : First made available on ORBilu
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/47848
Explaining Defect Detection with Saliency Maps
English
Lorentz, Joe mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Hartmann, Thomas mailto [DataThings S.A.]
Moawad, Assaad mailto [DataThings S.A.]
Fouquet, Francois mailto [DataThings S.A.]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
2021
No
[en] xai ; saliency ; defect detection
[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.
http://hdl.handle.net/10993/47848
The final authenticated version is available online at https://doi.org/10.1007/978-3-030-79463-7_43
FnR ; FNR14297122 > Joe Lorentz > DETECT > Towards Edge-optimized Deep Learning For Explainable Quality Control > 01/01/2020 > 31/12/2023 > 2019

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