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http://dx.doi.org/10.15683/kosdi.2022.6.30.364

Development of AI Detection Model based on CCTV Image for Underground Utility Tunnel  

Kim, Jeongsoo (Korea Institute of Civil Engineering and Building Technology, Department of Future and Smart Construction)
Park, Sangmi (Korea Institute of Civil Engineering and Building Technology, Department of Future and Smart Construction)
Hong, Changhee (Korea Institute of Civil Engineering and Building Technology, Department of Future and Smart Construction)
Park, Seunghwa (Korea Institute of Civil Engineering and Building Technology, Department of Future and Smart Construction)
Lee, Jaewook (Korea Institute of Civil Engineering and Building Technology, Department of Future and Smart Construction)
Publication Information
Journal of the Society of Disaster Information / v.18, no.2, 2022 , pp. 364-373 More about this Journal
Abstract
Purpose: The purpose of this paper is to develope smoke detection using AI model for detecting the initial fire in underground utility tunnels using CCTV Method: To improve detection performance of smoke which is high irregular, a deep learning model for fire detection was trained to optimize smoke detection. Also, several approaches such as dataset cleansing and gradient exploding release were applied to enhance model, and compared with results of those. Result: Results show the proposed approaches can improve the model performance, and the final model has good prediction capability according to several indexes such as mAP. However, the final model has low false negative but high false positive capacities. Conclusion: The present model can apply to smoke detection in underground utility tunnel, fixing the defect by linking between the model and the utility tunnel control system.
Keywords
Underground utility tunnel; Smoke; Deep learning; Object detection; Irregular object;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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