YOLOv8 Based Early Land Fire Detection System Integrated with Telegram
DOI:
https://doi.org/10.33795/jartel.v16i3.10591Keywords:
Raspberry Pi, Flame, Image Processing, Yolov8, TelegramAbstract
This early land fire detection system uses a Raspberry Pi 4B device integrated with RGB- and HSV-based color filtering methods, as well as a target mask module. The Raspberry Pi system connects to Google Drive via an OAuth client to store detection results. Additionally, a device running the YOLOv8 algorithm is used for validation to determine whether detected objects are fires or not. The validation results from YOLOv8 are then sent to Telegram via a chat bot in daytime testing, the system used a confidence threshold of 0.25 and achieved a total confidence of 43.6%, an average transmission time of 5730.30 ms (approximately 5.73 seconds), and an accuracy rate of 70% for the captured images. Meanwhile, in nighttime testing, the accuracy increased to 86%, with an average confidence of 49.07% and an average detection time of 115.20 ms. However, the YOLOv8 algorithm can still produce false negatives if the test data or model training data lacks sufficient variation.
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