Deep Learning-Based Object Detection for Automated Disassembly of Control Cabinets
Scheck A, Bründl P, Franke J (2026)
Publication Type: Conference contribution
Publication year: 2026
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 497-508
Conference Proceedings Title: Lecture Notes in Mechanical Engineering
Event location: New York City, NY
ISBN: 9783032056092
DOI: 10.1007/978-3-032-05610-8_50
Abstract
The automation of the disassembly process for end-of-life products in the electrical industry is essential for establishing a sustainable circular economy. A central challenge in this context is the initial identification of installed components, as their wide variety imposes significant demands on conventional object recognition systems. Therefore, this paper investigates the effectiveness of a deep learning-based computer vision system for automating the identification and localization of electrical components in control cabinets. For this purpose, the models YOLOv8, YOLO11 and Co-DETR are implemented and compared. The evaluation is based on a specifically developed dataset containing images of typical electrical components of industrial control cabinets. The performance metrics achieved, including mean average precision values exceeding 95% and inference times of a few milliseconds, demonstrate the effectiveness of the selected object recognition models. Additionally, optical character recognition models are integrated to enable granular identification of components, achieving accuracies of over 97%. The proposed detection system provides new insights into the application of deep learning models for automated disassembly processes and marks another step towards a sustainable circular economy in the electrical industry.
Authors with CRIS profile
Albert Scheck
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS)
Patrick Bründl
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS)
Jörg Franke
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS)
How to cite
APA:
Scheck, A., Bründl, P., & Franke, J. (2026). Deep Learning-Based Object Detection for Automated Disassembly of Control Cabinets. In Krishnaswami Srihari, Mohammad T. Khasawneh, Sangwon Yoon, Daehan Won (Eds.), Lecture Notes in Mechanical Engineering (pp. 497-508). New York City, NY, US: Springer Science and Business Media Deutschland GmbH.
MLA:
Scheck, Albert, Patrick Bründl, and Jörg Franke. "Deep Learning-Based Object Detection for Automated Disassembly of Control Cabinets." Proceedings of the 34th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2025, New York City, NY Ed. Krishnaswami Srihari, Mohammad T. Khasawneh, Sangwon Yoon, Daehan Won, Springer Science and Business Media Deutschland GmbH, 2026. 497-508.
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