Robust Analog Gauge Reading via Virtual Point-Based Geometric Rectification and P2-YOLO-Pose

Automated reading of analog gauges in industrial environments is essential for predictive maintenance and safety monitoring. However, conventional computer vision approaches encounter two fundamental bottlenecks: polar unwrapping techniques induce severe nonlinear scaling distortions under oblique viewing angles and axis-aligned bounding boxes (AABBs) are geometrically inefficient for encapsulating high-aspect-ratio rotating needles. To overcome these limitations, this paper proposes a novel end-to-end framework that innovatively redefines gauge reading as a structural pose estimation task. We model each gauge as a topological five-keypoint skeleton ( $k_{start},k_{mid},k_{center},k_{end},k_{tip}$), and localize these landmarks using a customized P2-YOLO-Pose architecture. By integrating a high-resolution P2 feature layer (stride 4) while excising the macro-scale P5 layer, the network yields a 40% enhancement in small-gauge detection recall with a negligible (<1%) frame-rate degradation. Furthermore, to address the intrinsic lack of salient vertices in circular dials, we introduce a Virtual Point (VP) generation algorithm. This algorithm exploits the point symmetry of the detected keypoints to autonomously synthesize four spatial correspondences, thereby enabling markerless, homography-based perspective rectification for corner-free objects. An adaptive control mechanism based on aspect ratio analysis ( $AR\le 1.5$) dynamically regulates the geometric warping to prevent algorithmic over-correction. Extensive evaluations on an 11,000-image field dataset acquired from an operational power data center demonstrate a Pose mAP50 of 99.45% and an mAP50-95 of 99.37%. Under severe vertical tilt conditions, the VP-based rectification curtails the absolute reading error from 3.5% to 0.6% compared to the uncorrected baseline, attaining measurement precision commensurate with physical ArUco marker-based ground truths. Operating in real-time at 25.9 FPS, the proposed system is currently deployed within an integrated inspection platform coupled with an autonomous quadruped robot (Boston Dynamics SPOT), facilitating reliable, perspective-invariant visual inspections across 10 distinct classes of analog gauges in an active industrial facility.