Positive Weak Supervision Quality Increase by Consolidation for Acoustic Defect Detection in Concrete Structures

The aging of concrete social infrastructures such as tunnels, bridges, and highways is a growing concern worldwide. Those require careful inspection to ensure their users’ safety and traditional manual methods are not viable solutions due to the growing population of structures in need of testing and the manpower shortage. Among those inspection methods, the hammering test has been the focus of several previous works, including notably weakly supervised approaches. Those approaches query a human user on random audio sample pair similarity to transform the feature space into one suited for defect detection. However, the quality of the weak supervision obtained in such a way is often variable. Therefore, we propose a method to improve positive weak supervision quality by consolidating the dataset prior to the query process. Experiments conducted with concrete test blocks showed the effectiveness of our proposed method.