CRAX: Parameter-Efficient Fine-Tuning of SAM2 for Interactive Crack Annotation

We study how to adapt the Segment Anything Model 2 (SAM2) for interactive segmentation of thin, cracklike structures. Rather than building a fully automatic crack detector, we focus on the annotator’s perspective: how many clicks are needed to obtain boundary- and topology-faithful masks. To this end, we curate CRAX, a multi-domain corpus of 35 datasets covering surface cracks, retinal vessels, and plant roots/mycelium, together with leakage-controlled leave-one-domain-out and 5-fold leave-one-dataset-out splits tailored to interactive evaluation. On top of SAM2, we systematically compare three fine-tuning strategies: decoderonly, Low-Rank-Adaptation+decoder, and full encoder+decoder. Using a conservative click simulator and a topology-aware metric suite, we show that full fine-tuning substantially improves performance on challenging thin-structure domains, almost doubling boundary IoU and nearly halving Hausdorff distance in the hardest cross-domain setting, while consistently impro ving cross-dataset generalization within cracks. Most importantly, fine-tuned models reach—and often surpass—the baseline’s 9-click quality after only one to two clicks, reducing annotation effort and making SAM2 more effective for large-scale crack and crack-like labeling.