Cross-Scene Weakly Supervised Aircraft-Door Detection and Contour Extraction with Limited Pixel-Level Annotations

Reliable aircraft-door perception is essential for automatic passenger-boarding-bridge (PBB) docking, where small sill-localization errors can affect alignment safety. We formulate aircraft-door perception as cross-scene joint detection and contour extraction using abundant boxes and limited contour annotations. Unlike conventional region segmentation, the target is a thin, often open door-frame contour. CS-DoorContour combines box-supervised detection, soft-contour supervision, selectively valid region assistance, box projection, and uncertainty-aware cross-scene consistency. A versioned label pipeline converts antialiased annotations into one-pixel centerlines, soft distance targets, fixed-width bands, and region masks only when safe closure is possible. Capture-sequence-disjoint protocols evaluate in-domain, cross-camera, day-to-night, and leave-one-aircraft-instance-out generalization using boundary, distance, and docking-oriented geometry metrics. Across three seeds with 20 contour labels, the in-domain model obtains 93.92±3.23 Box AP, 0.876±0.002 BF1@2, 0.233±0.006 Boundary AP, and 1.47±0.22-pixel mean sill error. Across held-out-camera folds, Box AP ranges from 91.09 to 95.87 and BF1@2 from 0.829 to 0.855; held-out-aircraft folds obtain 72.30–86.43 Box AP and 0.755–0.865 BF1@2. The method reduces dense annotation requirements while preserving contour and sill geometry for practical docking.