Distress detection using egocentric videos for increasing discovery rate of novel distress during subway tunnel inspection

Abstract

In this paper, we propose a novel distress detection method using egocentric videos with the aim of increasing the discovery rate of novel distresses by novice engineers who fail to recognize them despite observing potential distress areas. In the proposed method, we introduce a mechanism that outputs an attention map that emphasizes potential distress areas into the deep learning model, which determines the presence or absence of distress from frames of egocentric videos taken by novice engineers during inspections. The proposed method enables high-precision distress detection and provides a basis for determining the results of detection. We confirm the effectiveness of our method through experiments using egocentric videos taken by actual the subway tunnel engineers.