IMU-Guided Adaptive Video Stabilization for Unmanned Surface Vehicles

This study addresses the critical challenge of video stabilization in Unmanned Surface Vehicles (USVs) operating under high-wave and harsh maritime conditions. Conventional stabilization methods often struggle to distinguish erratic vessel motions caused by waves from intentional camera maneuvers, leading to significant image distortion, horizon tilt, and loss of visual data. To overcome this, we propose a novel hybrid stabilization framework that fuses Inertial Measurement Unit (IMU) data with Optical Flow analysis. Unlike traditional fusion techniques, the core innovation of this approach is using IMU data strictly as a control signal to guide the stabilization process, rather than as a direct geometric warping tool. A key contribution of the proposed method is the implementation of an adaptive α (alpha) coefficient, which dynamically adjusts the weighting between inertial data and visual cues based on the instantaneous dynamics and motion intensity of the marine vehicle. This adaptive mechanism allows the algorithm to effectively suppress high-frequency jitter and wave-induced oscillations while preserving the integrity of intentional navigation maneuvers along the cruise path. Experimental results demonstrate that the proposed method significantly reduces unwanted vibrations and improves visual clarity compared to baseline stabilization models. The findings suggest that the IMU-guided adaptive approach provides a robust and computationally efficient solution for real-time perception and situational awareness in autonomous maritime systems. The source code and trained model generated in this study are publicly available at: https://github.