Structural health monitoring (SHM) is essential for the early detection of infrastructure defects, such as cracks in concrete bridge pier. but often faces challenges in efficiency and accuracy in complex environments. Although the Segment Anything Model (SAM) achieves excellent segmentation performance, its computational demands limit its suitability for real-time applications on edge devices. To address these challenges, this paper proposes Crack-EdgeSAM, a self-prompting crack segmentation system that integrates YOLOv8 for generating prompt boxes and a fine-tuned EdgeSAM model for crack segmentation. To ensure computational efficiency, the method employs ConvLoRA, a Parameter-Efficient Fine-Tuning (PEFT) technique, along with DiceFocalLoss to fine-tune the EdgeSAM model. Our experimental results on public datasets and the climbing robot automatic inspections demonstrate that the system achieves high segmentation accuracy and significantly enhanced inference speed compared to the most recent methods. Notably, the system processes 1024 x 1024 pixels images at 46 FPS on our PC and 8 FPS on Jetson Orin Nano.

翻译：结构健康监测对于基础设施缺陷（如混凝土桥墩裂纹）的早期检测至关重要，但在复杂环境中常面临效率与准确性的挑战。尽管Segment Anything Model（SAM）实现了卓越的分割性能，但其计算需求限制了其在边缘设备上实时应用的适用性。为应对这些挑战，本文提出Crack-EdgeSAM——一种集成YOLOv8生成提示框与微调EdgeSAM模型进行裂纹分割的自提示裂纹分割系统。为确保计算效率，该方法采用参数高效微调技术ConvLoRA并结合DiceFocalLoss对EdgeSAM模型进行微调。在公开数据集及爬壁机器人自动检测上的实验结果表明，相较于最新方法，本系统实现了高分割精度与显著提升的推理速度。值得注意的是，系统在PC端以46 FPS、在Jetson Orin Nano上以8 FPS的速度处理1024×1024像素图像。