Optimization of Computational Resources for Real-Time Product Quality Assessment Using Deep Learning and Multiple High Frame Rate Camera Sensors
Autor: | Adi Wibowo, Joga Dharma Setiawan, Hadha Afrisal, Anak Agung Sagung Manik Mahachandra Jayanti Mertha, Sigit Puji Santosa, Kuncoro Budhi Wisnu, Ambar Mardiyoto, Henri Nurrakhman, Boyi Kartiwa, Wahyu Caesarendra |
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Jazyk: | angličtina |
Rok vydání: | 2023 |
Předmět: | |
Zdroj: | Applied System Innovation, Vol 6, Iss 1, p 25 (2023) |
Druh dokumentu: | article |
ISSN: | 2571-5577 29636043 |
DOI: | 10.3390/asi6010025 |
Popis: | Human eyes generally perform product defect inspection in Indonesian industrial production lines; resulting in low efficiency and a high margin of error due to eye tiredness. Automated quality assessment systems for mass production can utilize deep learning connected to cameras for more efficient defect detection. However, employing deep learning on multiple high frame rate cameras (HFRC) causes the need for much computation and decreases deep learning performance, especially in the real-time inspection of moving objects. This paper proposes optimizing computational resources for real-time product quality assessment on moving cylindrical shell objects using deep learning with multiple HFRC Sensors. Two application frameworks embedded with several deep learning models were compared and tested to produce robust and powerful applications to assess the quality of production results on rotating objects. Based on the experiment results using three HFRC Sensors, a web-based application with tensorflow.js framework outperformed desktop applications in computation. Moreover, MobileNet v1 delivers the highest performance compared to other models. This result reveals an opportunity for a web-based application as a lightweight framework for quality assessment using multiple HFRC and deep learning. |
Databáze: | Directory of Open Access Journals |
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