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

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 unnecessary 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 also be converted to a tiny model to deploy it on microcontroller.