Similar damage image retreival by extraction of semantic features for bridgemaintenance

Abstract

Although it is important to refer to past damage cases in the bridge diagnosis, the method has not been established yet. Therefore, a system is required which can retrieve past similar damage images form a database using damage images as keys. In this study, a LSTM network was constructed to semantically extract damage features from captured images. Training was performed using actual bridge damage images, and similar image retrievals were conducted. As a result, by extracting semantic features using LSTM, the accuracy of similar image retrieval significantly improved compared to previous methods, enabling more useful similar image retrieval for bridge diagnosis.