A Risk Estimation Approach based on Deep Learning in Shipbuilding Industry
| Autor: | Youhee Choi, Jeong-Ho Park, Byung-Tae Jang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Estimation
business.industry Computer science Deep learning 0211 other engineering and technologies 02 engineering and technology 010501 environmental sciences 01 natural sciences Task (project management) Shipbuilding Risk analysis (engineering) Hazardous waste 021105 building & construction Accidents prevention Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | ICTC |
| DOI: | 10.1109/ictc46691.2019.8939725 |
| Popis: | Shipbuilding industry is one of the most hazardous industries. To reduce accidents, various safety policies and practices have been established and recommended to obey. Despite workers being made aware of risk associated with not following these practices, many workers do not follow these practices for various reasons such as inconvenience of wearing a personal safety equipment, increase of cost, insufficient working time, and so on. In addition, there are many cases that workers carry out a task or pass through without knowing risk of the task or risk of the area to be worked. It is difficult for each individual worker to know various surrounding circumstances, and there are also limitations in that safety supervisors play a role of safety management for all worksites. Therefore, we propose an automated risk estimation approach to support identifying hazardous zones and estimating risk by verifying whether safety measures are performed for the identified hazardous zone based on deep learning method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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