Structural health monitoring (SHM) is essential for ensuring the safety and longevity of infrastructure, but complex image environments, noisy labels, and reliance on manual damage assessments often hinder its effectiveness. This study introduces the Guided Detection Network (Guided-DetNet), a framework designed to address these challenges. Guided-DetNet is characterized by a Generative Attention Module (GAM), Hierarchical Elimination Algorithm (HEA), and Volumetric Contour Visual Assessment (VCVA). GAM leverages cross-horizontal and cross-vertical patch merging and cross-foreground-background feature fusion to generate varied features to mitigate complex image environments. HEA addresses noisy labeling using hierarchical relationships among classes to refine instances given an image by eliminating unlikely class instances. VCVA assesses the severity of detected damages via volumetric representation and quantification leveraging the Dirac delta distribution. A comprehensive quantitative study and two robustness tests were conducted using the PEER Hub dataset, and a drone-based application, which involved a field experiment, was conducted to substantiate Guided-DetNet's promising performances. In triple classification tasks, the framework achieved 96% accuracy, surpassing state-of-the-art classifiers by up to 3%. In dual detection tasks, it outperformed competitive detectors with a precision of 94% and a mean average precision (mAP) of 79% while maintaining a frame rate of 57.04fps, suitable for real-time applications. Additionally, robustness tests demonstrated resilience under adverse conditions, with precision scores ranging from 79% to 91%. Guided-DetNet is established as a robust and efficient framework for SHM, offering advancements in automation and precision, with the potential for widespread application in drone-based infrastructure inspections.

翻译：结构健康监测（SHM）对于保障基础设施的安全与耐久性至关重要，但复杂的图像环境、噪声标签以及对人工损伤评估的依赖常常制约其有效性。本研究提出了引导检测网络（Guided-DetNet），这是一个旨在应对上述挑战的框架。Guided-DetNet的核心特征包括生成注意力模块（GAM）、层级消除算法（HEA）以及体积轮廓视觉评估（VCVA）。GAM通过跨水平与跨垂直的图块合并以及跨前景-背景的特征融合，生成多样化特征以缓解复杂图像环境的影响。HEA利用类别间的层级关系，通过消除不可能的类别实例来精炼给定图像中的实例，从而处理噪声标签问题。VCVA借助狄拉克δ分布，通过体积表示与量化来评估检测到损伤的严重程度。研究利用PEER Hub数据集进行了全面的定量分析与两项鲁棒性测试，并通过一项涉及实地实验的无人机应用，验证了Guided-DetNet的优异性能。在三分类任务中，该框架达到了96%的准确率，比现有最优分类器高出最多3%。在双检测任务中，其以94%的精确率和79%的平均精度均值（mAP）优于同类检测器，同时保持57.04帧/秒的处理速度，适用于实时应用。此外，鲁棒性测试表明其在不利条件下仍具韧性，精确率介于79%至91%之间。Guided-DetNet被确立为一个鲁棒且高效的SHM框架，在自动化与精确性方面取得了进展，有望在无人机基础设施巡检中得到广泛应用。