# CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices

arXiv: https://arxiv.org/abs/2412.07205 · HTML: https://mlsanity.com/paper/2412.07205/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Parameter-Efficient Transfer Learning with Diff Pruning](https://mlsanity.com/paper/2012.07463/) (2020)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [Rewrite the Stars](https://mlsanity.com/paper/2403.19967/) (2024)
- [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)
- [ConvLoRA and AdaBN based Domain Adaptation via Self-Training](https://mlsanity.com/paper/2402.04964/) (2024)
- [Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model](https://mlsanity.com/paper/2409.09484/) (2024)

## Built on by (most cited first, 1 of 1)

- [Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges](https://mlsanity.com/paper/2507.09562/) (2025)

## Abstract (arXiv)

Structural Health Monitoring (SHM) is a sustainable and essential approach for infrastructure maintenance, enabling the early detection of structural defects. Leveraging computer vision (CV) methods for automated infrastructure monitoring can significantly enhance monitoring efficiency and precision. However, these methods often face challenges in efficiency and accuracy, particularly in complex environments. Recent CNN-based and SAM-based approaches have demonstrated excellent performance in crack segmentation, but their high computational demands limit their applicability on edge devices. This paper introduces CrackESS, a novel system for detecting and segmenting concrete cracks. The approach first utilizes a YOLOv8 model for self-prompting and a LoRA-based fine-tuned SAM model for crack segmentation, followed by refining the segmentation masks through the proposed Crack Mask Refinement Module (CMRM). We conduct experiments on three datasets(Khanhha's dataset, Crack500, CrackCR) and validate CrackESS on a climbing robot system to demonstrate the advantage and effectiveness of our approach.