Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Falling Objects Detection for Near Miss Incidents Identification on Construction Site
Falling objects accidents frequently occurs on construction site and cause a great deal of casualties every year. In addition to the reported injury accidents, there are still a large number of near miss incidents related to falling objects on construction site, since not all falling object incidents involves injuries because of sheer luck. Therefore, identifying the near miss incidents and their causes in time is an effective way to prevent a real injury accident. This paper proposes a deep learning-based video monitoring method to automatically detect and analyze the probable causes of the near miss incident (falling objects) on construction site, which forms an exhaustive case database from real-time monitoring for safety evaluation, training, and improvement. The proposed approach develops a comprehensive framework for near miss incident detection and cause analysis by advanced computer vision methods. The experiment results show that our approach achieves high performance in both falling object detection and probable causes classification.
Falling Objects Detection for Near Miss Incidents Identification on Construction Site
Falling objects accidents frequently occurs on construction site and cause a great deal of casualties every year. In addition to the reported injury accidents, there are still a large number of near miss incidents related to falling objects on construction site, since not all falling object incidents involves injuries because of sheer luck. Therefore, identifying the near miss incidents and their causes in time is an effective way to prevent a real injury accident. This paper proposes a deep learning-based video monitoring method to automatically detect and analyze the probable causes of the near miss incident (falling objects) on construction site, which forms an exhaustive case database from real-time monitoring for safety evaluation, training, and improvement. The proposed approach develops a comprehensive framework for near miss incident detection and cause analysis by advanced computer vision methods. The experiment results show that our approach achieves high performance in both falling object detection and probable causes classification.
Falling Objects Detection for Near Miss Incidents Identification on Construction Site
Li, Chengqian (Autor:in) / Ding, Lieyun (Autor:in)
ASCE International Conference on Computing in Civil Engineering 2019 ; 2019 ; Atlanta, Georgia
Computing in Civil Engineering 2019 ; 138-145
13.06.2019
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Falling Objects Detection for Near Miss Incidents Identification on Construction Site
| British Library Conference Proceedings | 2019
Construction site near-edge anti-falling ground floor board and system
| Europäisches Patentamt | 2021