Grad-CAM for Network Models: To Support Aerial Vision Based Wildfire Perception

In this paper, rethinking the application of deep neural network models in aerial image-based wildfire perception, a gradient-weighted class activation mapping (Grad-CAM) scheme is studied and tested on the model of you only look once (YOLO) for the drone-image-based wildfire detection. Grad-CAM helps the model to be more explainable and understandable. It also helps engineers and fire-fighters to view the production of target layer(s) of the wildfire detection model. Such a benefit is similar as higher-weighted segmentation models. Therefore, Grad-CAM could be a tool to help designing less-weighted wildfire detection models. It could be also used to support fire-fighters labeling the suspect wildfire area and make decision-making process more efficiently.