Structural integrity is vital for maintaining the safety and longevity of concrete infrastructures such as bridges, tunnels, and walls. Traditional methods for detecting damages like cracks and spalls are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study explores advanced data-driven techniques using deep learning for automated damage detection and analysis. Two state-of-the-art instance segmentation models, YOLO-v7 instance segmentation and Mask R-CNN, were evaluated using a dataset comprising 400 images, augmented to 10,995 images through geometric and color-based transformations to enhance robustness. The models were trained and validated using a dataset split into 90% training set, validation and test set 10%. Performance metrics such as precision, recall, mean average precision (mAP@0.5), and frames per second (FPS) were used for evaluation. YOLO-v7 achieved a superior mAP@0.5 of 96.1% and processed 40 FPS, outperforming Mask R-CNN, which achieved a mAP@0.5 of 92.1% with a slower processing speed of 18 FPS. The findings recommend YOLO-v7 instance segmentation model for real-time, high-speed structural health monitoring, while Mask R-CNN is better suited for detailed offline assessments. This study demonstrates the potential of deep learning to revolutionize infrastructure maintenance, offering a scalable and efficient solution for automated damage detection.

翻译：结构完整性对于维持桥梁、隧道和墙体等混凝土基础设施的安全与耐久性至关重要。检测裂缝和剥落等损伤的传统方法劳动密集、耗时且易受人为误差影响。为应对这些挑战，本研究探索了利用深度学习进行自动化损伤检测与分析的高级数据驱动技术。研究采用包含400张图像的原始数据集，通过几何变换与基于颜色的增强处理扩展至10,995张图像以提升模型鲁棒性，并评估了两种先进的实例分割模型：YOLO-v7实例分割与Mask R-CNN。数据集按90%训练集、10%验证/测试集的比例划分进行训练与验证。评估指标包括精确率、召回率、平均精度均值（mAP@0.5）和帧率（FPS）。YOLO-v7取得了96.1%的优异mAP@0.5值，处理速度达40 FPS；而Mask R-CNN的mAP@0.5为92.1%，处理速度较慢（18 FPS）。研究结果表明，YOLO-v7实例分割模型适用于实时高速结构健康监测，而Mask R-CNN更适合精细化的离线评估。本研究证明了深度学习在革新基础设施维护方面的潜力，为自动化损伤检测提供了可扩展的高效解决方案。