---
_id: '12904'
abstract:
- lang: eng
text: 'It is crucial to identify defective machine components in production to ensure
quality. Some components generate heat when defective, so automating the inspection
process with a thermal imaging camera can provide qualitative measurements. This
work aims to use computer vision methods to locate these components in thermal
images. Since there is currently no comparison of object detection and semantic
segmentation algorithms for this use case, this study compares different architectures
with the goal of localising these components for further defect inspection. Moreover,
as there are currently no datasets for this use case, this study contributes a
novel annotated dataset of thermal images of combine harvester components. The
different algorithms are evaluated based on the quality of their predictions and
their suitability for further defect inspection. As semantic segmentation and
object detection cannot be directly compared with each other, custom weighted
metrics are used. The architectures evaluated include RetinaNet, YOLOV8 Detector,
DeepLabV3+, and SegFormer. Based on the experimental results, semantic segmentation
outperforms object detection regarding the use case, and the SegFormer architecture
achieves the best results with a weighted MeanIOU of 0.853. '
author:
- first_name: Hanna
full_name: Senke, Hanna
id: '79810'
last_name: Senke
- first_name: Dennis
full_name: Sprute, Dennis
last_name: Sprute
- first_name: Ulrich
full_name: Büker, Ulrich
id: '81453'
last_name: Büker
orcid: 0000-0002-4403-3889
- first_name: Holger
full_name: Flatt, Holger
id: '58494'
last_name: Flatt
citation:
ama: Senke H, Sprute D, Büker U, Flatt H. Deep Learning-Based Localisation of
Combine Harvester Components in Thermal Images. (Längle T, Heizmann M, Karlsruher
Institut für Technologie. Institut für Industrielle Informationstechnik , Fraunhofer-Institut
für Optronik, Systemtechnik und Bildauswertung , eds.). KIT Scientific Publishing;
2024:71-82. doi:10.58895/ksp/1000174496-7
apa: Senke, H., Sprute, D., Büker, U., & Flatt, H. (2024). Deep learning-based
localisation of combine harvester components in thermal images. In T. Längle,
M. Heizmann, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik
, & Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Eds.),
Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024 (pp. 71–82). KIT
Scientific Publishing. https://doi.org/10.58895/ksp/1000174496-7
bjps: 'Senke H et al. (2024) Deep Learning-Based Localisation of
Combine Harvester Components in Thermal Images, Längle T et al. (eds). Karlsruhe:
KIT Scientific Publishing.'
chicago: 'Senke, Hanna, Dennis Sprute, Ulrich Büker, and Holger Flatt. Deep Learning-Based
Localisation of Combine Harvester Components in Thermal Images. Edited by
Thomas Längle, Michael Heizmann, Karlsruher Institut für Technologie. Institut
für Industrielle Informationstechnik , and Fraunhofer-Institut für Optronik, Systemtechnik
und Bildauswertung . Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024.
Karlsruhe: KIT Scientific Publishing, 2024. https://doi.org/10.58895/ksp/1000174496-7.'
chicago-de: 'Senke, Hanna, Dennis Sprute, Ulrich Büker und Holger Flatt. 2024. Deep
learning-based localisation of combine harvester components in thermal images.
Hg. von Thomas Längle, Michael Heizmann, Karlsruher Institut für Technologie.
Institut für Industrielle Informationstechnik , und Fraunhofer-Institut für Optronik,
Systemtechnik und Bildauswertung . Forum Bildverarbeitung 2024 = Image Pocessing
Forum 2024. Karlsruhe: KIT Scientific Publishing. doi:10.58895/ksp/1000174496-7,
.'
din1505-2-1: 'Senke, Hanna ; Sprute, Dennis ; Büker,
Ulrich ; Flatt, Holger ;
Längle, T. ; Heizmann,
M. ; Karlsruher Institut für Technologie.
Institut für Industrielle Informationstechnik ; Fraunhofer-Institut
für Optronik, Systemtechnik und Bildauswertung (Hrsg.): Deep learning-based
localisation of combine harvester components in thermal images. Karlsruhe :
KIT Scientific Publishing, 2024'
havard: H. Senke, D. Sprute, U. Büker, H. Flatt, Deep learning-based localisation
of combine harvester components in thermal images, KIT Scientific Publishing,
Karlsruhe, 2024.
ieee: 'H. Senke, D. Sprute, U. Büker, and H. Flatt, Deep learning-based localisation
of combine harvester components in thermal images. Karlsruhe: KIT Scientific
Publishing, 2024, pp. 71–82. doi: 10.58895/ksp/1000174496-7.'
mla: Senke, Hanna, et al. “Deep Learning-Based Localisation of Combine Harvester
Components in Thermal Images.” Forum Bildverarbeitung 2024 = Image Pocessing
Forum 2024, edited by Thomas Längle et al., KIT Scientific Publishing, 2024,
pp. 71–82, https://doi.org/10.58895/ksp/1000174496-7.
short: H. Senke, D. Sprute, U. Büker, H. Flatt, Deep Learning-Based Localisation
of Combine Harvester Components in Thermal Images, KIT Scientific Publishing,
Karlsruhe, 2024.
ufg: 'Senke, Hanna u. a.: Deep learning-based localisation of combine harvester
components in thermal images, hg. von Längle, Thomas u. a., Karlsruhe 2024.'
van: 'Senke H, Sprute D, Büker U, Flatt H. Deep learning-based localisation of combine
harvester components in thermal images. Längle T, Heizmann M, Karlsruher Institut
für Technologie. Institut für Industrielle Informationstechnik , Fraunhofer-Institut
für Optronik, Systemtechnik und Bildauswertung , editors. Forum Bildverarbeitung
2024 = Image Pocessing Forum 2024. Karlsruhe: KIT Scientific Publishing; 2024.'
conference:
end_date: 2024-11-22
location: Karlsruhe
name: Forum Bildverarbeitung 2024
start_date: 2024-11-21
corporate_editor:
- 'Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik '
- 'Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung '
date_created: 2025-05-08T14:01:20Z
date_updated: 2025-05-12T07:33:48Z
department:
- _id: DEP5023
doi: 10.58895/ksp/1000174496-7
editor:
- first_name: Thomas
full_name: Längle, Thomas
last_name: Längle
- first_name: Michael
full_name: Heizmann, Michael
last_name: Heizmann
keyword:
- industrial quality assurance
- deep learning architectures
- object localisation
- Thermal images
language:
- iso: eng
page: 71-82
place: Karlsruhe
publication: Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024
publication_identifier:
isbn:
- 978-3-7315-1386-5
publication_status: published
publisher: KIT Scientific Publishing
quality_controlled: '1'
status: public
title: Deep learning-based localisation of combine harvester components in thermal
images
type: conference_editor_article
user_id: '83781'
year: '2024'
...
---
_id: '550'
abstract:
- lang: eng
text: Additive Manufacturing (AM) technologies are increasingly used for final part
production. Especially technologies for processing of metal, like Selective LaserMelting
(SLM), arefocusedin this area. The shift from prototyping towards final part
production results in enhanced requirements for repeatability and predictability
of the part quality. Machine manufacturers offer process monitoring solutions
for different aspects of the production process, like the powder bed surface,
the melt pool, and the laser energy. Nevertheless, the significance of these systems
is not fully proven and threshold values for the monitored process parameters
have to be determined for each product individually. This impedes the development
of suitable process control systems. The paper gives an overview ofexistingresearch
approaches and available process monitoring systems for SLM and their applicability
for predicting certain part characteristics. The existing solutions are evaluated
based on own research results. Next, AM specific difficulties for the development
of process control tools and possible solutions are discussed.
author:
- first_name: Andrea
full_name: Huxol, Andrea
id: '43559'
last_name: Huxol
- first_name: Franz-Josef
full_name: Villmer, Franz-Josef
id: '14290'
last_name: Villmer
citation:
ama: 'Huxol A, Villmer F-J. Process Control for Selective Laser Melting - Opprtunities
and Limitations. In: Villmer F-J, Padoano E, Department of Production Engineering
and Management, eds. Production Engineering and Management. Lemgo; 2018:17-28.'
apa: Huxol, A., & Villmer, F.-J. (2018). Process Control for Selective Laser
Melting - Opprtunities and Limitations. In F.-J. Villmer, E. Padoano, & Department
of Production Engineering and Management (Eds.), Production Engineering and
Management (pp. 17–28). Lemgo.
bjps: Huxol A and Villmer F-J (2018) Process Control for Selective Laser
Melting - Opprtunities and Limitations. In Villmer F-J, Padoano E and Department
of Production Engineering and Management (eds), Production Engineering and
Management. Lemgo, pp. 17–28.
chicago: Huxol, Andrea, and Franz-Josef Villmer. “Process Control for Selective
Laser Melting - Opprtunities and Limitations.” In Production Engineering and
Management, edited by Franz-Josef Villmer, Elio Padoano, and Department of
Production Engineering and Management, 17–28. Lemgo, 2018.
chicago-de: 'Huxol, Andrea und Franz-Josef Villmer. 2018. Process Control for Selective
Laser Melting - Opprtunities and Limitations. In: Production Engineering and
Management, hg. von Franz-Josef Villmer, Elio Padoano, und Department of Production
Engineering and Management, 17–28. Lemgo.'
din1505-2-1: 'Huxol, Andrea ; Villmer, Franz-Josef: Process Control
for Selective Laser Melting - Opprtunities and Limitations. In: Villmer,
F.-J. ; Padoano, E. ; Department of Production Engineering and Management
(Hrsg.): Production Engineering and Management. Lemgo, 2018, S. 17–28'
havard: 'A. Huxol, F.-J. Villmer, Process Control for Selective Laser Melting -
Opprtunities and Limitations, in: F.-J. Villmer, E. Padoano, Department of Production
Engineering and Management (Eds.), Production Engineering and Management, Lemgo,
2018: pp. 17–28.'
ieee: A. Huxol and F.-J. Villmer, “Process Control for Selective Laser Melting -
Opprtunities and Limitations,” in Production Engineering and Management,
Lemgo, 2018, no. 1, pp. 17–28.
mla: Huxol, Andrea, and Franz-Josef Villmer. “Process Control for Selective Laser
Melting - Opprtunities and Limitations.” Production Engineering and Management,
edited by Franz-Josef Villmer et al., no. 1, 2018, pp. 17–28.
short: 'A. Huxol, F.-J. Villmer, in: F.-J. Villmer, E. Padoano, Department of Production
Engineering and Management (Eds.), Production Engineering and Management, Lemgo,
2018, pp. 17–28.'
ufg: 'Huxol, Andrea/Villmer, Franz-Josef (2018): Process Control for Selective
Laser Melting - Opprtunities and Limitations, in: Franz-Josef Villmer et. al.
(Hgg.): Production Engineering and Management, Lemgo, S. 17–28.'
van: 'Huxol A, Villmer F-J. Process Control for Selective Laser Melting - Opprtunities
and Limitations. In: Villmer F-J, Padoano E, Department of Production Engineering
and Management, editors. Production Engineering and Management. Lemgo; 2018. p.
17–28.'
conference:
end_date: 2018-10-05
location: Lemgo
name: Proceedings 8th International Conference
start_date: 2018-10-04
corporate_editor:
- Department of Production Engineering and Management
- Hochschule Ostwestfalen-Lippe
date_created: 2019-02-13T13:55:29Z
date_updated: 2023-03-15T13:50:00Z
department:
- _id: DEP1306
editor:
- first_name: Franz-Josef
full_name: Villmer, Franz-Josef
last_name: Villmer
- first_name: Elio
full_name: Padoano, Elio
last_name: Padoano
issue: '1'
keyword:
- Additive manufacturing
- Process capability
- Process monitoring
- Quality assurance
- Final part production
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.hs-owl.de/fileadmin/diman/Veroeffentlichungen/PEM2018_proceedings_web.pdf
oa: '1'
page: 17-28
place: Lemgo
publication: Production Engineering and Management
publication_identifier:
isbn:
- 978-3-946856-03-0
publication_status: published
related_material:
link:
- relation: contains
url: https://www.hs-owl.de/fileadmin/diman/Veroeffentlichungen/PEM2018_proceedings_web.pdf
status: public
title: Process Control for Selective Laser Melting - Opprtunities and Limitations
type: conference
user_id: '45673'
year: 2018
...