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http://dx.doi.org/10.7837/kosomes.2021.27.1.022

Fundamental Study on Algorithm Development for Prediction of Smoke Spread Distance Based on Deep Learning  

Kim, Byeol (Graduate school of Korea Maritime and Ocean University)
Hwang, Kwang-Il (Division of Mechanical Engineering, Korea Maritime and Ocean University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.27, no.1, 2021 , pp. 22-28 More about this Journal
Abstract
This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.
Keywords
Deep learning; Smoke detection; Smoke spread distance prediction; YOLO; LSTM;
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