Estimation of contact accident risk based on recurrent neural network introducing spatial-temporal attention

Abstract

In this paper, we propose a method for estimating the contact accidents risk with heavy machinery to support safety management on construction sites. According to recent reports on occupational accidents, since the construction industry experiences a high number of incidents, preventing contact accidents between heavy machinery and workers, which are on the increase, is a crucial task. The proposed method constructs a deep learning model to estimate the contact accident risk by using videos obtained from multiple viewpoints of cameras mounted on heavy machinery or fixed-point cameras at a construction site. By inputting visual information of videos obtained via spatial-temporal attention to a recurrent neural network, it is possible to accurately estimate the risk of contact accidents. At the end of this paper, we can verify the effectiveness of the proposed method through experiments using videos taken at actual construction sites.