Attention on Classification for Fire Segmentation
This work addresses fire detection for safety applications, but it is incremental as it builds on existing segmentation and attention techniques.
The paper tackles fire segmentation in images by proposing a CNN that jointly performs classification and segmentation, using spatial self-attention and a channel attention module based on classification probability, resulting in improved performance over previous methods.
Detection and localization of fire in images and videos are important in tackling fire incidents. Although semantic segmentation methods can be used to indicate the location of pixels with fire in the images, their predictions are localized, and they often fail to consider global information of the existence of fire in the image which is implicit in the image labels. We propose a Convolutional Neural Network (CNN) for joint classification and segmentation of fire in images which improves the performance of the fire segmentation. We use a spatial self-attention mechanism to capture long-range dependency between pixels, and a new channel attention module which uses the classification probability as an attention weight. The network is jointly trained for both segmentation and classification, leading to improvement in the performance of the single-task image segmentation methods, and the previous methods proposed for fire segmentation.