# Rapid-Deployment Crack Measurement Based on SAM3 Semantic-Edge Response Decoding

arXiv: https://arxiv.org/abs/2607.12292 · HTML: https://mlsanity.com/paper/2607.12292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 45 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Extract Free Dense Labels from CLIP](https://mlsanity.com/paper/2112.01071/) (2021)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Side Adapter Network for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.12242/) (2023)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [SegFix: Model-Agnostic Boundary Refinement for Segmentation](https://mlsanity.com/paper/2007.04269/) (2020)
- [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019)
- [Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2404.08181/) (2024)
- [In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2408.04961/) (2024)
- [CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2411.10086/) (2024)
- [RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth](https://mlsanity.com/paper/2108.00616/) (2021)
- [SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures](https://mlsanity.com/paper/2503.01113/) (2025)
- [Fine-tuning vision foundation model for crack segmentation in civil infrastructures](https://mlsanity.com/paper/2312.04233/) (2023)
- [SAM-based instance segmentation models for the automation of structural damage detection](https://mlsanity.com/paper/2401.15266/) (2024)
- [ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation](https://mlsanity.com/paper/2506.21233/) (2025)
- [Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture](https://mlsanity.com/paper/2007.00477/) (2020)
- [A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation](https://mlsanity.com/paper/2303.01582/) (2023)
- [Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation](https://mlsanity.com/paper/2408.12815/) (2024)
- [Target Refocusing via Attention Redistribution for Open-Vocabulary Semantic Segmentation: An Explainability Perspective](https://mlsanity.com/paper/2511.16170/) (2025)
- [What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification](https://mlsanity.com/paper/2202.03714/) (2022)
- [Segmentation of Tubular Structures Using Iterative Training with Tailored Samples](https://mlsanity.com/paper/2309.08727/) (2023)
- [MixerCSeg: An Efficient Mixer Architecture for Crack Segmentation via Decoupled Mamba Attention](https://mlsanity.com/paper/2603.01361/) (2026)
- [Weakly-Supervised Crack Detection](https://mlsanity.com/paper/2206.06743/) (2022)

## Abstract (arXiv)

Reliable crack measurement is essential for infrastructure condition assessment, yet existing image-based approaches typically depend on pixel-wise annotations, task-specific segmentation training, and mask-based geometric measurement, making cross-scene deployment costly and sensitive to segmentation errors. We identify an output-interface mismatch in SAM3: its prompt-conditioned semantic response preserves crack evidence that is often suppressed or spatially distorted in the final candidate masks. Across six public crack datasets, the internal response achieves 82.66% average crack-pixel recall, compared with 74.66% for the retained SAM3 proposals, with an average mismatch ratio of 8.73%. Based on this observation, we propose Semantic-Edge Response Decoding (SERD) to calibrate the semantic response using a fixed Sobel structural field, and further develop SERD-DQ, a training-free framework that directly estimates crack centerline and transverse geometry from the continuous decoded response without generating an intermediate predicted mask. Experiments verify both segmentation fidelity and direct geometric measurement against manually established pixel-level references. Compared with native SAM3 mask-based quantification, SERD-DQ reduces width MAE from 6.072 to 5.547 pixels, length relative error from 22.245% to 17.355%, and area relative error from 33.921% to 26.228%, while achieving a latent geometry recovery rate of 0.394. The results indicate that continuous semantic-edge responses provide a more reliable interface for training-free crack quantification than conventional mask-mediated measurement.