2026/07/14 by Shipeng Liu, Zhanping Song, Liang Zhao +1
Computer Science · #cs.CV
arxiv created 2026/07/30 · arxiv updated 2026/07/31
Cross-domain infrastructure crack segmentation is challenged by variations in materials, imaging conditions, crack morphology, and background interference. Although text-promptable foundation models reduce the need for task-specific training, SAM3's native proposal interface may not fully expose the crack evidence already encoded by the frozen model. We identify and quantify an output-interface mismatch in which the internal language-conditioned response preserves ground-truth-aligned crack evidence that is absent from the native proposal output. Based on this finding, we propose Semantic-Edge Response Decoding (SERD), a training-free method that replaces proposal-based readout with direct response decoding. SERD normalizes the dense response, calibrates it using a fixed Sobel prior, and applies a single threshold to produce the crack mask. SAM3 remains frozen without any target-domain annotations. Using one source domain solely for threshold selection, SERD achieves an average Crack IoU of 58.00% on five unseen datasets, compared with 54.33% for native SAM3. Across six rotated source-domain settings, it obtains a mean target-domain IoU of 60.23% and a Boundary F1 of 67.18%, exceeding SAM3 by 3.27 and 2.70 percentage points, respectively. The results show that task-aligned internal-response decoding provides a simple and transferable solution for cross-domain infrastructure crack segmentation. \textitCode is available at: \hrefhttps://github.com/xauat-liushipeng/SERDGitHub.