Neuronal Networks for Visual Inspection of Assembly Completeness and Correctness in Manufacturing

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dc.contributor.authorBaykar, Ali Ömer
dc.contributor.authorKural, Ayhan
dc.contributor.authorLambrecht, Jens
dc.date.accessioned2025-02-20T08:46:29Z
dc.date.available2025-02-20T08:46:29Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.description7th International Conference on Green Technology and Sustainable Development, GTSD 2024 -- 25 July 2024 through 26 July 2024 -- Ho Chi Minh -- 324489en_US
dc.description.abstractThe conformity of the quality with the desired specifications in production must be controlled quickly, reliably and accurately. Cost reduction and efficiency studies in production quality control stages are of great importance today. For this reason, non-human and intelligent automated systems are the main research subjects as a solution method in quality control stages. In this study, the final visual inspection of fastening elements of an industrial product is addressed. The inspection of connection elements, such as screws, as one of these quality control stages, is presented through a framework utilizing a camera and learnable neural network, replacing human-eye control. Fasteners can be counted as small objects in the images obtained. Therefore, in this study, object detectors based on different CNN backbones (ResNet 50–101) and proposals are discussed and their performance in detecting these small objects is compared to achieve the high detection speed, accuracy and reliability. To address the challenges at an industrial level for object detection methods, a non-processed image dataset has been created. This dataset aims to represent various lighting conditions, including dark-bright fields and diffuse reflection, as well as occlusion and restricted camera angles. During the training phase, hyperparameter-tuning optimization of deep networks such as YOLOv8, Faster-RCNN with ResNet50&101 and lastly Sparse-RCNN with a different set of learned object proposals is evaluated, which can be most suitable for the detection of screw connection. Experimental results show that the pretrained Faster-RCNN and Sparse RCNN has over the % 85 success rate of detection of small objects in an industrial environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
dc.identifier.doi10.1007/978-3-031-76197-3_4
dc.identifier.endpage49en_US
dc.identifier.isbn978-303176196-6
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85214126640
dc.identifier.scopusqualityQ4
dc.identifier.startpage41en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-76197-3_4
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1726
dc.identifier.volume1195 LNNSen_US
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofLecture Notes in Networks and Systems
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250220
dc.subjectConvolutional Neural Networken_US
dc.subjectObject Detectionen_US
dc.subjectScrew Connectionen_US
dc.subjectSmall Object Detectionen_US
dc.subjectVisual Inspectionen_US
dc.titleNeuronal Networks for Visual Inspection of Assembly Completeness and Correctness in Manufacturing
dc.typeConference Object

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