A computer vision approach with OpenCV and deep learning for determining inductance in planar coils
Abstract
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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