Underwater defects in piers pose potential hazards to the safety and durability of river‐crossing bridges. The concealment and difficulty in detecting underwater defects often result in their oversight. Acoustic methods face challenges in directly achieving accurate measurements of underwater defects, while optical methods are time‐consuming. This study proposes a two‐step rapid inspection method for underwater concrete bridge piers by combining acoustics and optics. The first step combines macroscopic sonar scanning with an enhanced YOLOv7 to detect and locate piers and defects. Second, the camera approaches the defects for image acquisition, and an enhanced DeepLabv3+ is used for defect identification. The results demonstrate an average mean average precision@0.5 of 95.10% for defect and pier detection, and an mean intersection over union of 0.914 for exposed reinforcement and spalling identification. The method was applied to a real river‐crossing bridge and reduced inspection time by 51.2% compared to traditional methods for assessing a row of 11 piers.

中文翻译：

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结合声呐、相机和深度学习的水下混凝土桥梁结构两步快速检测


桥墩的水下缺陷对跨河桥梁的安全性和耐用性构成潜在危害。隐藏性和难以检测水下缺陷往往会导致他们的疏忽。声学方法在直接实现水下缺陷的准确测量方面面临挑战，而光学方法则非常耗时。本研究提出了一种结合声学和光学的水下混凝土桥墩两步快速检查方法。第一步将宏观声纳扫描与增强的 YOLOv7 相结合，以检测和定位桥墩和缺陷。其次，相机接近缺陷进行图像采集，并使用增强的 DeepLabv3+ 进行缺陷识别。结果表明，缺陷和桥墩检测的平均平均 precision@0.5 为 95.10%，暴露钢筋和剥落识别的平均交 0.914。该方法应用于真正的过河桥，与评估一排 11 个桥墩的传统方法相比，检查时间缩短了 51.2%。