A computer vision approach with OpenCV and deep learning for determining inductance in planar coils
Introduction/purpose: In the realm of development and use of computer vision and AI methodologies, this research introduces a combination and advanced method using YOLOv9, a deep learning concept of whole image processing in one pass through a convolutional neural network (CNN) and the OpenCV Py...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Defence in Belgrade
2024-10-01
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| Series: | Vojnotehnički Glasnik |
| Subjects: | |
| Online Access: | https://scindeks.ceon.rs/article.aspx?artid=0042-84692404645B |
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| Summary: | Introduction/purpose: In the realm of development and use of computer
vision and AI methodologies, this research introduces a combination and
advanced method using YOLOv9, a deep learning concept of whole image
processing in one pass through a convolutional neural network (CNN) and
the OpenCV Python image processing library to determine the geometry of
planar coils. These geometric parameters are the main parameters used to
calculate the inductance value using Mohan's formula, which exclusively
utilizes only geometric data to estimate inductance values. This method
significantly speeds up the verification and calculation processes, while also
playing a role in improving quality control after manufacturing.
Methods: The methodology is divided into two main phases. Initially, a
YOLOv9 model was trained for object recognition using a generated
synthetic dataset of coil shapes created with Python's Turtle graphics
library. Then, after the detection phase, OpenCV was used to identify the
geometric parameters of the images. The pixels were converted into
millimeters using a ratio method to calculate the inductance value
accurately.
Results: The YOLOv9 model successfully identified various planar coil
shapes, and the geometric parameters were identified through OpenCV.
Subsequently, the inductance was successfully calculated.
Conclusion: The results show that the proposed method is a novel and
effective way of calculating inductance. |
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| ISSN: | 0042-8469 2217-4753 |