Real‐Time Vision System for Estimating Magnetic Compass Needle Oscillation Using Deep Learning

Compass needle oscillations reduce navigation accuracy and increase stabilization time. Existing estimation methods lack automation, robustness, and real‐time capability. This paper presents a real‐time, noncontact vision system for estimating the oscillation parameters of a magnetic compass needle using deep learning. The system employs a camera to continuously capture images of the needle. A lightweight convolutional neural network (YOLO11s) is used to detect the pivot and tip positions in real time. From these positions, the angular displacement is computed and tracked over time. Two mathematical models—a standard damped sinusoidal model and a modified oscillation model—are applied to fit the angle‐time data and estimate key oscillation parameters, including amplitude, angular frequency, and damping coefficient. The proposed system is evaluated against traditional image processing and object tracking methods under various lighting conditions, camera resolutions, and frame rates. The results demonstrate high detection accuracy (mAP 99.5% at IoU 0.5), strong tracking robustness (MOTA 95.7%, MODA 99.9%), and real‐time processing at 18 FPS. The modified model reduces fitting residuals and achieves average parameter estimation errors below 4%. The proposed framework provides an automated, high‐precision alternative to traditional methods and shows strong potential for embedded navigation systems, robotics stabilization, and physics education tools.