DeepSafety: A Deep Learning Framework for Unsafe Behaviors Detection of Steel Activity in Construction Projects

摘要

In the field of construction, the safety of construction workers on construction sites has been a major problem over the years. According to statistic of previous study, approximately 88% of these accidents are caused by unsafe behaviors. In recent years, the development and application of deep learning have attracted considerable research interest such as object detection, human pose estimation, etc. Meanwhile, the object detection techniques have been applied to detect whether workers wear or use proper equipment. However, using proper equipment doesn’t mean performing correct operation. Therefore, this paper proposes a deep learning framework, DeepSafty, to detect unsafe behaviors of construction workers through their postures to reduce their mortality rate. In our proposed DeepSafty, the object detection model YOLOv3 is used to locate construction workers precisely. Then, human pose estimation technology is used to determine various postures of construction workers. There are 17 joints in the poses. In other words, a 51-dimensional vector will be produced from the neural network, as unsafe behaviors are continual movements in the field of construction. Finally, a model that deals with time series in deep learning, i.e., long short-term memory, is used to solve any classification problems of time-dependent joint vectors. Finally, we conduct a comprehensive experimental study based on a dataset collected through a CCTV of a real construction site. The results demonstrate the effectiveness of our deep learning approach and show the strength of taking both object detection and human pose estimation into account in unsafe behaviors of construction workers.