A Defect Detection Algorithm for Optoelectronic Detectors Utilizing GLV-YOLO
Photodetectors are indispensable in a multitude of applications, with the detection of surface defects serving as a cornerstone for their production and advancement. To meet the demands of real-time and accurate defect detection, this paper introduces an optimization algorithm based on the GLV-YOLO...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/16/3/267 |
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| Summary: | Photodetectors are indispensable in a multitude of applications, with the detection of surface defects serving as a cornerstone for their production and advancement. To meet the demands of real-time and accurate defect detection, this paper introduces an optimization algorithm based on the GLV-YOLO model tailored for photodetector defect detection in manufacturing settings. The algorithm achieves a reduction in the model complexity and parameter count by incorporating the GhostC3_MSF module. Additionally, it enhances feature extraction capabilities with the integration of the LSKNet_3 attention mechanism. Furthermore, it improves generalization performance through the utilization of the WIoU loss function, which minimizes geometric penalties. The experimental results showed that the proposed algorithm achieved 98.9% accuracy, with 2.1 million parameters and a computational cost of 7.0 GFLOPs. Compared to other methods, our approach outperforms them in both performance and efficiency, fulfilling the real-time and precise defect detection needs of photodetectors. |
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| ISSN: | 2072-666X |