Bidirectional Global-Edge Refinement Network for Aerial Infrared Segmentation in Automated Industrial Inspection

Accurate aircraft-region segmentation in aerial infrared imagery can provide a useful scene-understanding component for image-assisted aviation inspection. However, target scale variation and ambiguous boundaries remain challenging in simulated aerial infrared scenes. To address these issues, this paper develops a bidirectional collaborative framework that couples global semantic fusion with edge optimization. The Semantics-guided Multi-scale Context Fusion branch uses adaptive multi-scale feature extraction and cross-stage calibration to produce a reliable initial segmentation, while the Edge-Sensitive Superpixel Collaborative Refinement branch improves boundary localization through semantic-guided superpixel consistency. Their interaction forms a bidirectional closed loop in which semantic context guides edge refinement and refined boundaries feed back to semantic learning. Experiments on AISD, a dedicated physics-based simulated aerial infrared dataset, achieve an 86.29% mPA, 83.35% mF1, and 76.20% mIoU. Relative to the strongest baseline, P-Mamba, the gains are 0.89 percentage points in mPA, 1.52 points in mF1, and 0.88 points in mIoU, and a sourcewise holdout comparison indicates consistent performance under unseen source content within the AISD simulation pipeline. These results concern aircraft-region segmentation rather than direct diagnosis of thermal defects.