[{"title":"Deep learning-based localisation of combine harvester components in thermal images","citation":{"chicago":"Senke, Hanna, Dennis Sprute, Ulrich Büker, and Holger Flatt. <i>Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images</i>. Edited by Thomas Längle, Michael Heizmann, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik , and Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung . <i>Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024</i>. Karlsruhe: KIT Scientific Publishing, 2024. <a href=\"https://doi.org/10.58895/ksp/1000174496-7\">https://doi.org/10.58895/ksp/1000174496-7</a>.","apa":"Senke, H., Sprute, D., Büker, U., &#38; 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 , &#38; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung  (Eds.), <i>Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024</i> (pp. 71–82). KIT Scientific Publishing. <a href=\"https://doi.org/10.58895/ksp/1000174496-7\">https://doi.org/10.58895/ksp/1000174496-7</a>","ufg":"<b>Senke, Hanna u. a.</b>: Deep learning-based localisation of combine harvester components in thermal images, hg. von Längle, Thomas u. a., Karlsruhe 2024.","bjps":"<b>Senke H <i>et al.</i></b> (2024) <i>Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images</i>, Längle T et al. (eds). Karlsruhe: KIT Scientific Publishing.","mla":"Senke, Hanna, et al. “Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images.” <i>Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024</i>, edited by Thomas Längle et al., KIT Scientific Publishing, 2024, pp. 71–82, <a href=\"https://doi.org/10.58895/ksp/1000174496-7\">https://doi.org/10.58895/ksp/1000174496-7</a>.","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.","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.","chicago-de":"Senke, Hanna, Dennis Sprute, Ulrich Büker und Holger Flatt. 2024. <i>Deep learning-based localisation of combine harvester components in thermal images</i>. Hg. von Thomas Längle, Michael Heizmann, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik , und Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung . <i>Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024</i>. Karlsruhe: KIT Scientific Publishing. doi:<a href=\"https://doi.org/10.58895/ksp/1000174496-7\">10.58895/ksp/1000174496-7</a>, .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Senke, Hanna</span> ; <span style=\"font-variant:small-caps;\">Sprute, Dennis</span> ; <span style=\"font-variant:small-caps;\">Büker, Ulrich</span> ; <span style=\"font-variant:small-caps;\">Flatt, Holger</span> ; <span style=\"font-variant:small-caps;\">Längle, T.</span> ; <span style=\"font-variant:small-caps;\">Heizmann, M.</span> ; <span style=\"font-variant:small-caps;\">Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik </span> ; <span style=\"font-variant:small-caps;\">Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung </span> (Hrsg.): <i>Deep learning-based localisation of combine harvester components in thermal images</i>. Karlsruhe : KIT Scientific Publishing, 2024","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.","ieee":"H. Senke, D. Sprute, U. Büker, and H. Flatt, <i>Deep learning-based localisation of combine harvester components in thermal images</i>. Karlsruhe: KIT Scientific Publishing, 2024, pp. 71–82. doi: <a href=\"https://doi.org/10.58895/ksp/1000174496-7\">10.58895/ksp/1000174496-7</a>.","ama":"Senke H, Sprute D, Büker U, Flatt H. <i>Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images</i>. (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:<a href=\"https://doi.org/10.58895/ksp/1000174496-7\">10.58895/ksp/1000174496-7</a>"},"year":"2024","author":[{"full_name":"Senke, Hanna","last_name":"Senke","id":"79810","first_name":"Hanna"},{"first_name":"Dennis","full_name":"Sprute, Dennis","last_name":"Sprute"},{"last_name":"Büker","id":"81453","full_name":"Büker, Ulrich","first_name":"Ulrich","orcid":"0000-0002-4403-3889"},{"last_name":"Flatt","first_name":"Holger","full_name":"Flatt, Holger","id":"58494"}],"page":"71-82","status":"public","date_created":"2025-05-08T14:01:20Z","publication":"Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024","publication_identifier":{"isbn":["978-3-7315-1386-5"]},"date_updated":"2025-05-12T07:33:48Z","editor":[{"first_name":"Thomas","last_name":"Längle","full_name":"Längle, Thomas"},{"last_name":"Heizmann","full_name":"Heizmann, Michael","first_name":"Michael"}],"corporate_editor":["Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik ","Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung "],"language":[{"iso":"eng"}],"keyword":["industrial quality assurance","deep learning architectures","object localisation","Thermal images"],"publication_status":"published","conference":{"end_date":"2024-11-22","start_date":"2024-11-21","location":"Karlsruhe","name":"Forum Bildverarbeitung 2024"},"department":[{"_id":"DEP5023"}],"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.  "}],"quality_controlled":"1","publisher":"KIT Scientific Publishing","type":"conference_editor_article","user_id":"83781","place":"Karlsruhe","doi":"10.58895/ksp/1000174496-7","_id":"12904"},{"department":[{"_id":"DEP1306"}],"conference":{"start_date":"2018-10-04","end_date":"2018-10-05","name":"Proceedings 8th International Conference","location":"Lemgo"},"abstract":[{"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.","lang":"eng"}],"issue":"1","publication_status":"published","place":"Lemgo","_id":"550","user_id":"45673","type":"conference","page":"17-28","status":"public","main_file_link":[{"open_access":"1","url":"https://www.hs-owl.de/fileadmin/diman/Veroeffentlichungen/PEM2018_proceedings_web.pdf"}],"year":2018,"author":[{"first_name":"Andrea","full_name":"Huxol, Andrea","id":"43559","last_name":"Huxol"},{"first_name":"Franz-Josef","id":"14290","last_name":"Villmer","full_name":"Villmer, Franz-Josef"}],"citation":{"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.","ufg":"<b>Huxol, Andrea/Villmer, Franz-Josef (2018)</b>: Process Control for Selective Laser Melting - Opprtunities and Limitations, in: Franz-Josef Villmer et. al. (Hgg.): <i>Production Engineering and Management</i>, Lemgo, S. 17–28.","bjps":"<b>Huxol A and Villmer F-J</b> (2018) Process Control for Selective Laser Melting - Opprtunities and Limitations. In Villmer F-J, Padoano E and Department of Production Engineering and Management (eds), <i>Production Engineering and Management</i>. Lemgo, pp. 17–28.","mla":"Huxol, Andrea, and Franz-Josef Villmer. “Process Control for Selective Laser Melting - Opprtunities and Limitations.” <i>Production Engineering and Management</i>, edited by Franz-Josef Villmer et al., no. 1, 2018, pp. 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.","apa":"Huxol, A., &#38; Villmer, F.-J. (2018). Process Control for Selective Laser Melting - Opprtunities and Limitations. In F.-J. Villmer, E. Padoano, &#38; Department of Production Engineering and Management (Eds.), <i>Production Engineering and Management</i> (pp. 17–28). Lemgo.","chicago":"Huxol, Andrea, and Franz-Josef Villmer. “Process Control for Selective Laser Melting - Opprtunities and Limitations.” In <i>Production Engineering and Management</i>, edited by Franz-Josef Villmer, Elio Padoano, and Department of Production Engineering and Management, 17–28. Lemgo, 2018.","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.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Huxol, Andrea</span> ; <span style=\"font-variant:small-caps;\">Villmer, Franz-Josef</span>: Process Control for Selective Laser Melting - Opprtunities and Limitations. In: <span style=\"font-variant:small-caps;\">Villmer, F.-J.</span> ; <span style=\"font-variant:small-caps;\">Padoano, E.</span> ; <span style=\"font-variant:small-caps;\">Department of Production Engineering and Management</span> (Hrsg.): <i>Production Engineering and Management</i>. Lemgo, 2018, S. 17–28","chicago-de":"Huxol, Andrea und Franz-Josef Villmer. 2018. Process Control for Selective Laser Melting - Opprtunities and Limitations. In: <i>Production Engineering and Management</i>, hg. von Franz-Josef Villmer, Elio Padoano, und Department of Production Engineering and Management, 17–28. Lemgo.","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. <i>Production Engineering and Management</i>. Lemgo; 2018:17-28.","ieee":"A. Huxol and F.-J. Villmer, “Process Control for Selective Laser Melting - Opprtunities and Limitations,” in <i>Production Engineering and Management</i>, Lemgo, 2018, no. 1, pp. 17–28."},"title":"Process Control for Selective Laser Melting - Opprtunities and Limitations","language":[{"iso":"eng"}],"keyword":["Additive manufacturing","Process capability","Process monitoring","Quality assurance","Final part production"],"oa":"1","date_updated":"2023-03-15T13:50:00Z","related_material":{"link":[{"relation":"contains","url":"https://www.hs-owl.de/fileadmin/diman/Veroeffentlichungen/PEM2018_proceedings_web.pdf"}]},"editor":[{"first_name":"Franz-Josef","full_name":"Villmer, Franz-Josef","last_name":"Villmer"},{"full_name":"Padoano, Elio","last_name":"Padoano","first_name":"Elio"}],"corporate_editor":["Department of Production Engineering and Management","Hochschule Ostwestfalen-Lippe"],"date_created":"2019-02-13T13:55:29Z","publication_identifier":{"isbn":["978-3-946856-03-0"]},"publication":"Production Engineering and Management"}]