FireWarn: Fire Hazards Detection Using Deep Learning Models

Abstract

Hazardous situations such as house, car, or forest fires may be recorded by cameras long before they are identified by people. To test whether deep learning could be used to quickly detect fires, we performed a series of experiments to detect the presence of fire or smoke in images and labeled them with bounding boxes. Two custom datasets were created in this research: a fire image classification dataset, and a fire and smoke detection and localization dataset. The first one only classifies the whole image, while the detection set further provides information about where the fire or smoke is within the image. We explore the efficacy of a basic convolutional classification neural network, which proved effective for fire classification, but show that pretrained classification models such as ResNet improves the accuracy when classifying fire and non-fire images. The pretrained model achieves 97.14% testing accuracy on our fire classification dataset. For fire detection and localization, three models were trained on images of fire and smoke to find and label the regions of interest. Results show that Faster R-CNN did not perform very well on fire detection and localization, while EfficientDet and YoloV5 performed much better. Moreover, YoloV5 using low resolution images also performed well on smoke detection and localization, which is more difficult than fire. YoloV5 achieved an average precision of 46.6 on our fire and smoke detection dataset.