发表机构
University of Hawaii at Manoa; Chittagong University of Engineering & Technology (CUET)（夏威夷大学马诺阿分校; 吉大港工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究设计并实现了一种约25美元的低成本爬壁机器人，结合YOLOv8、CNN和EfficientNet-B0进行混凝土裂缝检测，采用漏斗形机身降低44%功耗，实现高效、可扩展的实时基础设施检测。
AI 中文摘要
裂缝检测是确保建筑物及其他基础设施安全性和耐久性的关键过程。在本文中，我们开发了一种低成本的自动化裂缝检测机器人，利用CNN、EfficientNet-B0和YOLOv8对混凝土表面的裂缝进行高效识别，并引入了一个精选的裂缝图像数据集以支持训练和评估。YOLOv8的实时目标检测增强了裂缝定位能力，而CNN和EfficientNet-B0提供二分类，确保高精确率和召回率。该系统由两个阶段组成。在第一阶段，YOLOv8从视频帧中检测并定位墙面区域，并裁剪出边界框。第二阶段通过分析裁剪区域，使用三种模型之一进行裂缝检测。在机器人设计中也采用了经济高效的方法。该机器人采用基于风扇的负压吸附系统、四轮滑移转向驱动，以及ESP32-CAM用于实时图像采集。其轻量化的3D打印底盘确保了稳定性，使其能够在墙壁和天花板上移动，同时捕获图像用于裂缝分析。与传统爬壁机器人设计不同，该机器人采用了漏斗形机身，增强了负压产生能力，并实现了占空比降低44%，显著降低了功耗。通过将低成本硬件与深度学习流程相结合，我们的系统以约25美元的总成本提供了一种可扩展、高效且易于获取的实时基础设施检测解决方案，并附带一个轻量级Web应用程序，支持基于智能手机的控制。这种经济性使该系统更适合发展中国家，在这些国家，基础设施检测往往受到预算限制、劳动强度大和安全风险的制约。
英文摘要
Crack detection is a crucial process to ensure the safety and longevity of buildings and other infrastructure. In this paper, we developed a low-cost, automated crack detection robot that leverages CNN, EfficientNet-B0, and YOLOv8 for efficient identification of cracks in concrete surfaces with a curated crack image dataset introduced to support training and evaluation. YOLOv8's real-time object detection enhances crack localization, while CNN and EfficientNet-B0 provide binary classification, ensuring high precision and recall. The system consists of two stages. In the first stage, YOLOv8 detects and localizes wall regions from the video frame, and the bounding boxes are cropped. The second stage performs crack detection using one of three models by analyzing the cropped regions. Cost-effective approaches are also taken for robot design. The robot features a fan-based negative pressure adhesion system, a 4-wheeled skid-steering drive, and an ESP32-CAM for real-time image capture. Its lightweight 3D-printed chassis ensures stability, allowing it to navigate both walls and ceilings while capturing images for crack analysis. Unlike conventional wall-climbing robot designs, this robot incorporates a funnel-shaped body that enhances negative pressure generation and achieves a 44% reduction in duty cycle, significantly lowering power consumption. By combining low-cost hardware with a deep learning pipeline, our system provides a scalable, efficient, and accessible solution for real-time infrastructure inspection at an approximate total cost of $25, with a lightweight web application enabling smartphone-based control. This affordability makes the system more suitable for the developing world, where infrastructure inspection is often limited by budget constraints, labor intensity, and safety risks.
