Domain adaptation for acoustic inspection of concrete structures

本研究旨在实现混凝土结构锤击检测的自动化。监督学习方法存在的问题是，当系统的训练和部署之间的具体结构不同时，性能会下降（域间隙）。今年，努力已经转向基于深度学习的离群点检测方法，即Autoencoder类型的方法，该方法在该领域受到关注。目前正在研究将这些方法与薄弱的监督框架相结合的可能性。

This research aims at the automation of the hammering test for the inspection of concrete structures. Supervised learning approaches have theissue that when the concrete structure differs between training and deployment of the system, the performance is degraded (domain gap).This year, efforts have been shifted towards approaches based on Deep Learning for outlier detection, namely Autoencoder-type approaches, which have been gaining attention in the field. The potential of combining such methods with the weak supervision framework are being currently investigated.