| dc.contributor | 資科系 | - |
| dc.creator (作者) | 彭彥璁 | - |
| dc.creator (作者) | Peng, Yan-Tsung | - |
| dc.creator (作者) | Chang, Ray Hsienho;Choi, Seongchul;Cai, Changjie | - |
| dc.date (日期) | 2022-07 | - |
| dc.date.accessioned | 6-Feb-2023 14:30:27 (UTC+8) | - |
| dc.date.available | 6-Feb-2023 14:30:27 (UTC+8) | - |
| dc.date.issued (上傳時間) | 6-Feb-2023 14:30:27 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/143299 | - |
| dc.description.abstract (摘要) | This research discusses how to use a real-time Artificial Intelligence (AI) object detection model to improve on-site incident command and personal accountability in fire response. We utilized images of firegrounds obtained from an online resource and a local fire department to train the AI object detector, YOLOv4. Consequently, the real-time AI object detector can reach more than ninety percent accuracy when counting the number of fire trucks and firefighters on the ground utilizing images from local fire departments. Our initial results indicate AI provides an innovative method to maintain fireground personnel accountability at the scenes of fires. By connecting cameras to additional emergency management equipment (e.g., cameras in fire trucks and ambulances or drones), this research highlights how this technology can be broadly applied to various scenarios of disaster response, thus improving on-site incident fire command and enhancing personnel accountability on the fireground. | - |
| dc.format.extent | 103 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Emergency Management Science and Technology, Vol.2, Article number: 7 | - |
| dc.subject (關鍵詞) | Artificial Intelligence; Fire response activities; Firefighters; Fire apparatus; On-site Supervision; Deep learning | - |
| dc.title (題名) | Applying Artificial Intelligence (AI) to improve fire response activities | - |
| dc.type (資料類型) | article | - |
| dc.identifier.doi (DOI) | 10.48130/EMST-2022-0007 | - |
| dc.doi.uri (DOI) | https://doi.org/10.48130/EMST-2022-0007 | - |
