IoT-Based Smoking Violation Detection System Equipped with Object Detection Using YOLOv5s Algorithm
DOI:
https://doi.org/10.30871/jaic.v9i3.8459Keywords:
MQ-7, MQ-135, Smoking Violation Detection System, Telegram, YOLOv5sAbstract
Smoking is a common habit in Indonesia. The Indonesian government has implemented regulations on smoke-free areas, but violations of the smoke-free policy still often occur. Previous studies have developed smoking violation detection system based on the MQ sensor. However, the smoking violation detection system based only on the MQ sensor is less reliable because the detected gas could come from other sources. Therefore, this study discusses a smoking violation detection system that can automatically verify smoking violation activities using the MQ-7 sensor, MQ-135 sensor, and the YOLOv5s algorithm. The MQ-7 sensor that has been calibrated to detect CO in ppm units achieved an accuracy level of 89.84%. The MQ-135 sensor also has successfully detected ammonia and toluene in cigarette smoke in ppm units. The trained YOLOv5s algorithm achieved an average Precision of 91.9%, Recall 83.7%, F1-Score 87.6%, and mAP50 88.3%. The system is equipped with a speaker that will sound automatically after a verified smoking violation occurs and Telegram notifications in the form of text messages and images.
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