---
_id: '33510'
abstract:
- lang: eng
  text: In the manufacture of real wood products, defects can quickly occur during
    the production process. To quickly sort out these defects, a system is needed
    that finds damage in the irregularly structured surfaces of the product. The difficulty
    in this task is that each surface is visually different and no standard defects
    can be defined. Thus, damage detection using correlation does not work, so this
    paper will test different machine learning methods. To evaluate different machine
    learning methods, a data set is needed. For this reason, the available samples
    were recorded manually using a static fixed camera. Subsequently, the images were
    divided into sub-images, which resulted in a relatively small data set. Next,
    a convolutional neural network (CNN) was constructed to classify the images. However,
    this approach did not lead to a generalized solution, so the dataset was hashed
    using the a- and pHash. These hash values were then trained with a fully supervised
    system that will later serve as a reference model, in the semi-supervised learning
    procedures. To improve the supervised model and not have to label every data point,
    semi-supervised learning methods are used in the following. For this purpose,
    the CEAL method (wrapper method) is considered in the first and then the Π-Model
    (intrinsically semi-supervised).
author:
- first_name: Tom
  full_name: Sander, Tom
  last_name: Sander
- first_name: Sven
  full_name: Lange, Sven
  id: '38240'
  last_name: Lange
- first_name: Ulrich
  full_name: Hilleringmann, Ulrich
  last_name: Hilleringmann
- first_name: Volker
  full_name: Geneiß, Volker
  last_name: Geneiß
- first_name: Christian
  full_name: Hedayat, Christian
  last_name: Hedayat
- first_name: Harald
  full_name: Kuhn, Harald
  last_name: Kuhn
citation:
  ama: 'Sander T, Lange S, Hilleringmann U, Geneiß V, Hedayat C, Kuhn H. Detection
    of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised
    Learning Methods. In: <i>2022 Smart Systems Integration (SSI)</i>. IEEE; 2022.
    doi:<a href="https://doi.org/10.1109/ssi56489.2022.9901433">10.1109/ssi56489.2022.9901433</a>'
  apa: Sander, T., Lange, S., Hilleringmann, U., Geneiß, V., Hedayat, C., &#38; Kuhn,
    H. (2022). Detection of Defects on Irregularly Structured Surfaces using Supervised
    and Semi-Supervised Learning Methods. <i>2022 Smart Systems Integration (SSI)</i>.
    2022 Smart Systems Integration (SSI), Grenoble, France. <a href="https://doi.org/10.1109/ssi56489.2022.9901433">https://doi.org/10.1109/ssi56489.2022.9901433</a>
  bibtex: '@inproceedings{Sander_Lange_Hilleringmann_Geneiß_Hedayat_Kuhn_2022, place={Grenoble,
    France}, title={Detection of Defects on Irregularly Structured Surfaces using
    Supervised and Semi-Supervised Learning Methods}, DOI={<a href="https://doi.org/10.1109/ssi56489.2022.9901433">10.1109/ssi56489.2022.9901433</a>},
    booktitle={2022 Smart Systems Integration (SSI)}, publisher={IEEE}, author={Sander,
    Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat,
    Christian and Kuhn, Harald}, year={2022} }'
  chicago: 'Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneiß, Christian
    Hedayat, and Harald Kuhn. “Detection of Defects on Irregularly Structured Surfaces
    Using Supervised and Semi-Supervised Learning Methods.” In <i>2022 Smart Systems
    Integration (SSI)</i>. Grenoble, France: IEEE, 2022. <a href="https://doi.org/10.1109/ssi56489.2022.9901433">https://doi.org/10.1109/ssi56489.2022.9901433</a>.'
  ieee: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, and H. Kuhn,
    “Detection of Defects on Irregularly Structured Surfaces using Supervised and
    Semi-Supervised Learning Methods,” presented at the 2022 Smart Systems Integration
    (SSI), Grenoble, France, 2022, doi: <a href="https://doi.org/10.1109/ssi56489.2022.9901433">10.1109/ssi56489.2022.9901433</a>.'
  mla: Sander, Tom, et al. “Detection of Defects on Irregularly Structured Surfaces
    Using Supervised and Semi-Supervised Learning Methods.” <i>2022 Smart Systems
    Integration (SSI)</i>, IEEE, 2022, doi:<a href="https://doi.org/10.1109/ssi56489.2022.9901433">10.1109/ssi56489.2022.9901433</a>.
  short: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, H. Kuhn, in:
    2022 Smart Systems Integration (SSI), IEEE, Grenoble, France, 2022.'
conference:
  end_date: 2022-04-28
  location: Grenoble, France
  name: 2022 Smart Systems Integration (SSI)
  start_date: 2022-04-27
date_created: 2022-10-04T11:35:55Z
date_updated: 2022-10-04T11:37:39Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/ssi56489.2022.9901433
keyword:
- Machine Learning
- CNN
- Hashing
- semi-supervised learning
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9901433
place: Grenoble, France
project:
- _id: '52'
  name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: 2022 Smart Systems Integration (SSI)
publication_status: published
publisher: IEEE
status: public
title: Detection of Defects on Irregularly Structured Surfaces using Supervised and
  Semi-Supervised Learning Methods
type: conference
user_id: '38240'
year: '2022'
...