Topology-constrained U-Net for delamination mapping in ballistic-damaged thin GFRP

Delamination severely degrades the residual strength of glass-fibre-reinforced plastic (GFRP) laminates impacted by ballistic projectiles. This study introduces TopoPrior-UNet, a U-Net variant named for its novel loss function that embeds Topological Prior knowledge (specifically, a persistent-homology shape-prior loss to enforce the global topological structure of the damage, and a level-set active-contour loss to refine fuzzy boundaries) to segment multi-layer delamination images from a digital single lens reflex camera (DSLR). An initial set of 24 raw panel images was expanded to a 480-image set through three augmentation schemes, enabling learning under extreme data scarcity. Compared with a vanilla U-Net, TopoPrior-UNet improved mean Intersection-over-Union to 0.9832 (+9.9pp) and mean Dice to 0.9915 (+5.3pp), achieving the best scores across six metrics. These gains stem from jointly refining fuzzy boundaries and enforcing global ply topology, as corroborated by an ablation study. The trained model operates in quasi-real-time (i.e., inference in seconds per image) and requires only surface imagery, offering a cost-effective and rapid alternative to traditional NDT methods like X-ray or ultrasonic inspection, which often require minutes to hours for acquisition. Such rapid, non-destructive quantification of delamination extent lays the foundation for on-site residual-strength estimation and timely structural maintenance. • TopoPrior-UNet: level-set contour loss + persistent-homology shape-prior integration. • mIoU 0.9832, mDice 0.9915; +9.9 and +5.3 pts over U-Net on GFRP delamination. • Robust learning from 24 images expanded to 480 via augmentation. • Back-lit DSLR imagery enables near-real-time delamination mapping. • Shows the value of morphology-aware priors for materials engineering tasks.