Research On Unsafe Behavior Recognition Of Worker Carried By Excavator Bucket Based On Sematic Web

The annual construction safety accidents in China are still showing an upward trend,and the main cause of these accidents is the unsafe behavior of workers.Taking the unsafe behavior of workers carried by excavator bucket as an example,a new computer visionbased approach to identify worker’s unsafe behavior is studied in this paper.Applying computer vision to identify unsafe behavior of workers on construction site can strengthen the external supervision of workers,which can reduce the occurrence of accidents to a certain extent.However,the existing visual-only recognition methods need a large amount of training data containing the behavior to extract a specific behavioral feature.This not only has difficulty in data collection and labeling,but also requires a higher accuracy for the visual algorithm to detect the features.With this in mind,a new recognition approach integrating sematic and vision is developed in this paper.It can utilize the sematic web technology to identify the unsafe behavior of workers without specific data training,so just detect the objects on the construction site accurately.The approach studied in this paper include:(1)Representing the knowledge of worker’s unsafe behaviors in a structured form of semantic web,in order to be reused by computer for semantic recognition;(2)Detecting excavator buckets and workers from construction image based on Faster R-CNN algorithm,in order that computer can obtain the visual information of construction site;(3)Integrating these two types of information in semantic data management system(graph database),and utilizing the data reasoning ability of the graph database to identify the unsafe behavior of worker carried by excavator bucket.First,extracting the semantic data from visual information and modeling it into a semantic web data model automatically.Then,designing the semantic data reasoning rules of worker’s unsafe behaviors with the structural knowledge.Finally,utilizing the data operation ability of the graph database to query the sematic data of the unsafe behavior in the semantic web data model.After all these processing,the unsafe behavior of workers carried by excavator bucket can be recognited from the image.In order to verify the validity of this approach,a subway construction project in Wuhan city was randomly selected as a case study in this paper.The results show that the recognition accuracy for the unsafe behavior from the construction site monitoring images is between 83.54% and 90.72%.It showed that the approach studied in this paper can be effectively applied to identify the unsafe behavior of workers carried by excavator bucket on the construction site.This paper provides a new perspective for the study of visual identification of worker’s unsafe behaviors,and it also provides a new technical approach for the informationization of safety monitoring on the construction site.