Multimodality fire and smoke detection system

Abstract

Early smoke and fire detection is extremely important to prevent serious consequences for humans and property. A common solution is to utilize physical sensors such as gas detection sensors, smoke detection sensors, and temperature detection sensors caused by fire. However, the detection time of physical sensors is slower than combining multiple cues, especially combining with computer vision. In this paper, we propose a multi-modal fire and smoke detection solution that combines physical sensors (Sensor) and image sensors (Camera). In particular, our proposed method applies artificial intelligence (AI) and Internet of Things (IoT) to detect smoke and fire in the indoor environment. The knowledge distillation algorithm (KD) transfers from the full version of YOLO teacher models to the reduced version of YOLO model, whose detection accuracy is 10% smaller than the full version. The KD model is simpler, so it has a faster response time than the full model up to 8.22 (ms) and 51.56 (ms) when it runs on GPU and CPU, respectively.