Automatic Defect Detection in Structures

使用弱监督的锤击测试的自动化首先在仅使用声学数据的初始框架中建立。在实验室条件下与混凝土测试块进行的实验表明，该方法非常有效的进行一个合适的区分缺陷和非缺陷锤击样本的特征空间的转换。在这些结果之后，锤击样本的位置信息，即，在结构被锤子击打的地方，使用照相机沿着沿着可见的裂缝位置收集。这被添加到先前提到的框架中，并且为系统提供了关于其缺陷检测能力以及针对人类用户产生的弱监督中的错误的鲁棒性的提高的性能。

Automation of the hammering test using weak supervision was first established in an initial framework only using acoustic data. Experiments conducted with concrete test blocks in laboratory conditions showed the approach extremely effective for conducting a transformation of the feature space into one appropriate for discriminating defect and non-defect hammering samples. Following those results, the position information of hammering samples, i.e., where the structure was hit with a hammer, was collected using a camera along visible crack positions. This was added into the previously mentioned framework and provided the system increased performance regarding its defect detection abilities as well as robustness against errors in weak supervision produced by human user.