【摘要】 带涂层钢结构亚表面缺陷的存在例如腐蚀、钢基体裂纹及涂层脱粘等，会对整体结构的性能产生影响，并加速涂层系统退化过程。因此，提出一种基于YOLO v5的带涂层钢结构亚表面缺陷脉冲涡流热成像智能检测方法。这一方法可以在不移除涂层的情况下自动检测带涂层钢结构亚表面缺陷，具有重要的工程应用价值。通过所提方法，在保留涂层的情况下，对带涂层钢结构中的腐蚀、裂纹、脱粘等亚表面缺陷进行智能检测。检测结果表明，本文所提方法能够精确地识别和分类带涂层钢结构的4种亚表面缺陷类型：钢基体裂纹、脱粘、严重质量损失（如腐蚀凹坑、腐蚀磨损）以及轻微质量损失（如腐蚀薄层）。4种缺陷类型的检测精度分别高达96%、97%、95%和93%，同时满足实时性检测需求。更多还原

【Abstract】 Subsurface defects in coated steel structures, such as corrosion, steel matrix cracks, and coating debonding, affect the overall structural performance and accelerate the degradation of coating systems.Therefore, this study proposes a YOLO v5-based intelligent detection method for pulsed eddy current thermography of subsurface defects in coated steel structures. This method can automatically detect subsurface defects in coated steel structures without removing the coating, which is of significant importance for engineering applications. The proposed method intelligently detects subsurface defects such as corrosion, cracks, and debonding in coated steel structures without removing the coating. The detection results show that the proposed method can accurately identify and classify four types of subsurface defects in coated steel structures: cracks in the steel matrix, debonding, severe quality loss(corrosion pits and corrosion abrasion), and slight quality loss(thin corrosion layers); the four defect types can be detected with accuracies of 96%, 97%, 95%, and 93%, respectively, while meeting real-time inspection requirements.更多还原