用深度学习提升结构裂缝检测精度与效率，尤其适用于低分辨率图像。
Determination Of Structural Cracks Using Deep Learning Frameworks
- 采用残差U-Net集成架构，结合卷积块元模型提升细节捕捉能力。
- 在低分辨率图像上表现更优，交并比（IoU）和DICE系数均超过传统模型。
- 适合需要自动化、高可靠性结构健康监测的工程应用。
结构裂缝检测对公共安全至关重要，可预防潜在结构失效危及生命。人工检测易受经验不足影响，存在速度慢、不一致及人为错误问题。本研究提出一种新型深度学习架构，旨在提升检测准确率与效率。实验采用多种残差U-Net配置，并将其与包含卷积块的元模型集成形成集成模型。该组合增强了预测性能，超越单一模型表现。评估基于交并比（IoU）与DICE系数，结果显示残差U-Net在低分辨率图像中优于传统U-Net和SegNet，集成模型性能最佳，验证了其在结构缺陷监测中的有效性，为更可靠的自动化系统提供了可能。
原文摘要 · Abstract (English)
Structural crack detection is a critical task for public safety as it helps in preventing potential structural failures that could endanger lives. Manual detection by inexperienced personnel can be slow, inconsistent, and prone to human error, which may compromise the reliability of assessments. The current study addresses these challenges by introducing a novel deep-learning architecture designed to enhance the accuracy and efficiency of structural crack detection. In this research, various configurations of residual U-Net models were utilized. These models, due to their robustness in capturing fine details, were further integrated into an ensemble with a meta-model comprising convolutional blocks. This unique combination aimed to boost prediction efficiency beyond what individual models could achieve. The ensemble's performance was evaluated against well-established architectures such as SegNet and the traditional U-Net. Results demonstrated that the residual U-Net models outperformed their predecessors, particularly with low-resolution imagery, and the ensemble model exceeded the performance of individual models, proving it as the most effective. The assessment was based on the Intersection over Union (IoU) metric and DICE coefficient. The ensemble model achieved the highest scores, signifying superior accuracy. This advancement suggests way for more reliable automated systems in structural defects monitoring tasks.
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