This paper presents an innovative solution to the issue of infrastructure deterioration in the U.S., where a significant portion of facilities are in poor condition, and over 130,000 steel bridges have exceeded their lifespan. Aging steel structures face corrosion and hidden defects, posing major safety risks. The Silver Bridge collapse, resulting from an undetected flaw, highlights the limitations of manual inspection methods, which often miss subtle or concealed defects. Addressing the need for improved inspection technology, this work introduces an AI-powered magnetic inspection robot. Equipped with magnetic wheels, the robot adheres to and navigates complex ferromagnetic surfaces, including challenging areas like vertical inclines and internal corners, enabling thorough, large-scale inspections. Utilizing MobileNetV2, a deep learning model trained on steel surface defects, the system achieved an 85% precision rate across six defect types. This AI-driven inspection process enhances accuracy and reliability, outperforming traditional methods in defect detection and efficiency. The findings suggest that combining robotic mobility with AI-based image analysis offers a scalable, automated approach to infrastructure inspection, reducing human labor while improving detection precision and the safety of critical assets.

翻译：本文针对美国基础设施老化问题提出创新解决方案。美国大量设施状况堪若，超过13万座钢桥已超设计使用年限。老化钢结构面临腐蚀与隐蔽缺陷，构成重大安全隐患。以银桥坍塌事件为例，未检测出的结构缺陷暴露了人工检测方法的局限性——常遗漏细微或隐蔽缺陷。为满足先进检测技术需求，本研究开发了基于人工智能的磁力检测机器人。该机器人配备磁力轮，可吸附并导航于复杂铁磁表面（包括垂直斜面与内角等挑战性区域），实现全面大规模检测。系统采用经钢材表面缺陷数据集训练的MobileNetV2深度学习模型，在六类缺陷检测中达到85%的精确率。这种AI驱动的检测流程显著提升了准确性与可靠性，在缺陷检测效率和效果上均超越传统方法。研究表明：机器人移动能力与基于AI的图像分析技术相结合，可为基础设施检测提供可扩展的自动化解决方案，在减少人力投入的同时提升检测精度与关键资产安全性。