FireVision: An Early Fire and Smoke Detection Platform Utilizing Mask-RCNN Deep Learning Inferences

This paper introduces FireVision, a novel detection platform and model designed for real-time fire detection and monitoring. The platform employs automatic drone flights to capture high-resolution images across both suburban and forest environments. It uses ensemble deep learning inference from Mask R-CNN weak learners to trigger alerts. These inferences are enhanced by the accurate detection capabilities of ResNet-50, ResNet-101, and ResNet-152 classifiers, which can be deployed either in the cloud or on the drone’s edge co-processing units. The authors also implemented an index, called the Fire Criticality Index, that uses detection bounds and masks to indicate the criticality of a fire event, as well as an automated drone path-planning algorithm for detecting a fire critical event. The authors conducted experiments with their proposed model using a mask-annotated dataset comprising of 12,000 images. They evaluated the model’s accuracy and inference speed across various cloud and edge computing setups. The experimental results revealed that ResNet-101 outperformed ResNet-50 by 5–12.5% in maP@0.5 mask accuracy and demonstrated an 18% increase in inference time when executed on the cloud and a 27% increase on the drone edge device GPU. For the ResNet-152 compared to ResNet-101, the map@0.5 increased by 0.5–1.2%. However, the ResNet-152 inference time was 9x slower in the cloud and 1.3x slower on the GPU.