轻量化裂缝分割系统，可在边缘设备高效运行
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices
- YOLOv8自提示+LoRA微调SAM，兼顾速度与精度
- 在3个数据集上平均mAP达91.2%，边缘设备实时推理
- 适合嵌入式巡检机器人，部署门槛低
结构健康监测（SHM）是基础设施维护的可持续且关键手段，可实现结构缺陷的早期发现。利用计算机视觉方法进行自动化监测能显著提升效率与精度，但复杂环境下常面临效率与准确率难题。近年基于CNN和SAM的方法在裂缝分割上表现优异，但计算开销大，难以在边缘设备应用。本文提出CrackESS，一种新型混凝土裂缝检测与分割系统：首先使用YOLOv8模型实现自提示，再通过LoRA微调的SAM模型完成裂缝分割，并引入提出的裂纹掩码精炼模块（CMRM）优化结果。在Khanhha数据集、Crack500和CrackCR三个数据集上进行实验，并在攀爬机器人系统上验证，充分展示了该方法的优势与有效性。
原文摘要 · Abstract (English)
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.
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