TY  - EJOU
AU  - Lee, Jaekyung 
AU  - Kim, Youngjun 
AU  - Ko, Byungsung 
AU  - Kim, Taewon 
AU  - Park, Jaeheon 
AU  - Lee, Jiwon 
AU  - Kim, Wonhee 

TI  - Robust Analog Gauge Reading via Virtual Point-Based Geometric Rectification and P2-YOLO-Pose
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 147
IS  - 1
SN  - 1526-1506

AB  - 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 (<mml:math id="mml-ieqn-1"><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>), 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 (<mml:math id="mml-ieqn-2"><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:mo>≤</mml:mo><mml:mn>1.5</mml:mn></mml:math>) 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.
KW  - Analog gauge; deep learning; keypoint detection; geometric rectification; Industrial Internet of Things (IIoT); pose estimation

DO  - 10.32604/cmes.2026.080624