Enhancing Fire Alarm Systems Using Edge Machine Learning for Smoke Classification and False Alarm Reduction

A Traditional fire alarm systems use smoke sensors to monitor the concentration of smoke particles in the air. If the concentration exceeds a certain threshold, an alarm signal is triggered. However, this detection process could lead to false fire alarms, causing unnec-essary evacuations and panic among residents. False alarms may result from activities such as smoking in non-smoking areas, burning Oud, or cooking smoke. In this study, a deep neural network (DNN) model was trained to classify three types of smokes that were Oud, Cigarette, and burning tissue smokes. The offline prediction accuracy of this model was 97.5%. The size of the model after converting it to TensorFlow lite was 4.7 Kbytes. It can be also converted to tiny model to deploy it on microcontroller.