Fire and Smoke Segmentation using FireNet Combined with UNet3+

Fire is a major hazard in sensitive environments and can cause irreparable financial and life losses. In addition, fire in the forest and residential areas is considered a threatening event for natural and human resources. Accordingly, detecting fires and smoke in a timely and accurate manner is crucial in preventing financial losses, injuries, and fatalities. Since smoke can be detected before visible flames, smoke detection is a critical component of many fire alarm systems. Sensors sensitive to smoke and fire have the ability to detect these two events, but implementing a huge network of sensors in an open space like a forest is not economical. There are various methods for detecting fire and smoke, and among these, the methods based on deep learning exhibit bigger advantages in terms of accuracy and speed in segmentation. In this paper, we proposed some deep neural networks for fire and smoke detection. These are based on UNet, UNet++, and UNet3+. A proposed FireNet and five other structures are tried as the encoder’s backbone to segment fire and smoke. To train the models, 1200 images gathered from Internet images and videos were prepared, with appropriate labels for smoke and fire applied to their pixels. Experiments show that the best IoU (88.33%) is achieved by UNet++ with EfficientNet.B0 backbone. In small-scale fires, UNet with FireNet has the best performance, and when computational cost is important, UNet3+ with FireNet as the encoder’s backbone is the optimal choice.