Deep object detection framework for automated quality inspection in assembly operations

Author:

Basamakis Fotios Panagiotis,Bavelos Angelos Christos,Dimosthenopoulos Dimosthenis,Papavasileiou Apostolis,Makris Sotiris

Funder

H2020

European Commission

Publisher

Elsevier BV

Subject

General Medicine

Reference29 articles.

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2. S. Makris. Cooperating Robots for Flexible Manufacturing, doi: 10.1007/978-3-030-51591-1; 2020.

3. An optical system for identifying and classifying defects of metal parts;Papavasileiou;Int. J. Comput. Integr. Manuf.,2022

4. AI-enhanced vision system for dispensing process monitoring and quality control in manufacturing of large parts;Prezas;Procedia CIRP,2022

5. K. Fredriksson and E. Ukkonen, “E.: Faster template matching without FFT,” in: Proc. IEEE Intl. Conf. on Image Processing (ICIP). Volume, pp. 678–681.

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1. Enhancing Assembly Defect Detection from Object Detection to Image Classification;Materials Science Forum;2023-12-14

2. Intelligent approach for the industrialization of deep learning solutions applied to fault detection;Expert Systems with Applications;2023-12

3. Real-Time Defect and Object Detection in Assembly Line: A Case for In-Line Quality Inspection;Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems;2023-08-24

4. Object detection model-based quality inspection using a deep CNN;Sixteenth International Conference on Quality Control by Artificial Vision;2023-07-28

5. Computer vision for automatic defect detection of self-pierce rivet joints;Procedia CIRP;2023

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