Abstract
To reduce losses due to fire, it is necessary to extinguish and rescue immediately. However, in the dense area fire trucks were unable to reach the fire site due to narrow road access. In this case, drones that can fly by themselves to the point of fire then release fire-fighting bombs automatically can help fire disaster management. This means it needs a system where it can identify whether there is a fire. This study explores the idea of identifying fire using computer vision approach by making 8 identification models with each dataset of day, night, day, and night, thermal, day filter, night filter, day and night filter, and thermal filter, which had been tested by a set of data that corresponded to each dataset. YOLOv4 algorithm and Google Colaboratory were used, where each model took 8-10 hours to be trained. Results show that the day and night model was the most robust by having the highest average F1-score, 0.37. And will be performing the best on thermal data test with the value of F1-score is 0.6. This can be a representation for exploring new ideas on further study of how to obtain the most suitable dataset and data test.
Highlights
In the most populous province in Indonesia, DKI Jakarta, there have been around 6,429 fires throughout 2020 based on statistics. This dense area makes the distance between houses very close, which can accelerate the spread of fire from one house to another
This study explores the idea of identifying fire using computer vision approach by making 8 identification models with each dataset of day, night, day and night, thermal, day filtered, night filtered, day and night filtered, and thermal filtered, which had been tested by a set of data that corresponded to each dataset
This study explores the idea of identifying fire using computer vision approach by making 8 identification models tested with 8 data tests which the dataset and data test are days, night, day-night, thermal, day filter, night filter, day-night filter, and thermal filter
Summary
In the most populous province in Indonesia, DKI Jakarta, there have been around 6,429 fires throughout 2020 based on statistics. This dense area makes the distance between houses very close, which can accelerate the spread of fire from one house to another. The fire trucks were unable to reach the fire site due to the narrow road access In this case, drones that can fly by themselves to the point of fire release fire-fighting bombs automatically can help fire disaster management. Drones that can fly by themselves to the point of fire release fire-fighting bombs automatically can help fire disaster management This means it needs a system where it can identify whether there is a fire
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