Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks

Clicks: 252
ID: 272650
2021
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Abstract
In the context of the robotisation of industrial operations related to manipulating deformable linear objects, there is a need for sophisticated machine vision systems, which could classify the wiring harness branches and provide information on where to put them in the assembly process. However, industrial applications require the interpretability of the machine learning system predictions, as the user wants to know the underlying reason for the decision made by the system. We propose several different neural network architectures that are tested on our novel dataset to address this issue. We conducted various experiments to assess the influence of modality, data fusion type, and the impact of data augmentation and pretraining. The outcome of the network is evaluated in terms of the performance and is also equipped with saliency maps, which allow the user to gain in-depth insight into the classifier’s operation, including a way of explaining the responses of the deep neural network and making system predictions interpretable by humans.
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kicki2021sensorstell Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Piotr Kicki;Michał Bednarek;Paweł Lembicz;Grzegorz Mierzwiak;Amadeusz Szymko;Marek Kraft;Krzysztof Walas;Kicki, Piotr;Bednarek, Michał;Lembicz, Paweł;Mierzwiak, Grzegorz;Szymko, Amadeusz;Kraft, Marek;Walas, Krzysztof;
Journal sensors
Year 2021
DOI
10.3390/s21134327
URL
https://doi.org/10.3390/s21134327
Keywords
neural networks machine vision deformable linear objects robot learning computer vision for manufacturing

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