Keypoint-Based Geometric Anomaly Analysis Method for Antennas Using an EO Sensor Mounted on an Unmanned Vehicle

This study proposes a keypoint-based structural anomaly analysis system using Electro-Optical(EO) sensors mounted on unmanned ground vehicles. Unlike conventional object detection approaches, which struggle to interpret fine-grained structural deviations, the proposed method extracts keypoints of components and evaluates their geometric relationships—such as relative height ratios and tilt angles—to assess structural integrity. A three-stage pipeline consisting of object detection, keypoint detection, and post-processing validation is implemented. Experiments under various rotated conditions (0°, 90°, 135°, and 180°) show that the proposed method achieves error rates below 0.5 %, significantly outperforming conventional bounding box-based methods, which show over 50 % error. The system also demonstrates strong robustness against occlusion, viewpoint variation, and partial visibility. These results highlight the system's potential for real-time autonomous diagnostics, predictive maintenance, and quantitative evidence-based monitoring in dynamic field environments.