Real-Time Early Indoor Fire Detection and Localization on Embedded Platforms with Fully Convolutional One-Stage Object Detection

Fire disasters usually cause significant damage to human lives and property. Thus, early fire detection and localization in real time are crucial in minimizing fire disasters and reducing ecological losses. Studies of convolution neural networks (CNNs) show their capabilities in image processing tasks such as image classification, visual recognition, and object detection. Using CNNs for fire detection could improve detection accuracy. However, the high computational cost of CNNs requires an extensive training model size, making it difficult to deploy to resource-constrained edge devices. Moreover, the large size of the training model is challenging for real-time object detection. This paper develops a real-time early indoor fire-detection and -localization system that could be deployed on embedded platforms such as Jetson Nano. First, we propose a fully convolutional one-stage object detection framework for fire detection with real-time surveillance videos. The combination of backbone, path aggregation network, and detection head with generalized focal loss is used in the framework. We evaluate several networks as backbones and select the one with balanced efficiency and accuracy. Then we develop a fire localization strategy to locate the fire with two cameras in the indoor setting. Results show that the proposed architecture can achieve similar accuracy compared with the Yolo framework but using one-tenth of the model size. Moreover, the localization accuracy could be achieved within 0.7 m.

Automated summary

Fire disasters cause significant damage and early detection is crucial. Convolution neural networks (CNNs) have shown promise in fire detection but their computational cost and model size limit deployment. This paper presents a real-time indoor fire-detection and -localization system that addresses these challenges and achieves accurate and efficient results.