Morphology-Aware Wasserstein Distance Loss for Bounding-Box Regression in External Pipeline Coating Inspection

Ensuring the integrity of external anti-corrosion coatings is critical to the safe operation of long-distance oil and gas pipelines. Construction-site images contain complex backgrounds, multi-scale targets, and many elongated or weak-boundary coating conditions, challenging conventional bounding-box regression under low-overlap conditions. We constructed a real-site dataset of 1388 images and 1756 annotated instances across six coating-condition categories from long-distance natural-gas pipeline construction sites. Building on YOLOv10n, we formulate a Scale–Aspect Adaptive Normalized Wasserstein Distance (SA-NWD) regression strategy that adjusts the NWD weight according to target scale and aspect ratio during training. Across six random seeds, SA-NWD achieved the highest mean Recall (0.459 vs. 0.437 baseline) and mAP@0.5 (0.451 vs. 0.431), whereas fixed-weight NWD achieved the highest mean mAP@0.5:0.95 (0.251 vs. 0.240 baseline). Ablation results support complementary scale and aspect-ratio guidance. Morphology-grouped analysis showed higher Recall for extremely elongated targets (0.714 vs. 0.659 for fixed-weight NWD), with lower cross-seed standard deviation (±0.027 vs. ±0.101). These results suggest that SA-NWD may benefit high-recall screening of morphologically complex coating conditions, while fixed-weight NWD may provide more stable high-IoU localization; both strategies retain the inference architecture, parameter count, and GFLOPs of the YOLOv10n baseline.