论文标题
基于图像的施工过程中混凝土表面缺陷的检测
Image-based Detection of Surface Defects in Concrete during Construction
论文作者
论文摘要
缺陷会增加建筑项目的成本和持续时间，因为它们需要大量检查和文档工作。自动缺陷检测可以大大减少这些努力。这项工作的重点是检测蜂窝，这是可能影响结构完整性的混凝土结构中的实质缺陷。我们比较了从网络上刮除的蜂窝图像与从真实施工检查获得的图像。我们发现Web图像不会捕获在实际情况场景中发现的完整差异，并且该域中仍然缺乏数据。因此，我们的数据集可自由地用于进一步研究。训练了蜂窝检测的蒙版R-CNN和有效网络-B0。蒙版R-CNN模型允许基于实例分割检测蜂窝，而有效网络B0模型允许基于贴片的分类。我们的实验表明，这两种方法均适合求解和自动化蜂窝检测。将来，该解决方案可以将其纳入缺陷文档系统中。
Defects increase the cost and duration of construction projects as they require significant inspection and documentation efforts. Automating defect detection could significantly reduce these efforts. This work focuses on detecting honeycombs, a substantial defect in concrete structures that may affect structural integrity. We compared honeycomb images scraped from the web with images obtained from real construction inspections. We found that web images do not capture the complete variance found in real-case scenarios and that there is still a lack of data in this domain. Our dataset is therefore freely available for further research. A Mask R-CNN and EfficientNet-B0 were trained for honeycomb detection. The Mask R-CNN model allows detecting honeycombs based on instance segmentation, whereas the EfficientNet-B0 model allows a patch-based classification. Our experiments demonstrate that both approaches are suitable for solving and automating honeycomb detection. In the future, this solution can be incorporated into defect documentation systems.
