Evaluating Deep Neural Network-based Fire Detection for Natural Disaster Management

Recently, climate change has led to more frequent extreme weather events, introducing new challenges for Natural Disaster Management (NDM) organizations. This fact makes the employment of modern technological tools such as Deep Neural Networks-based fire detectors a necessity, as they can assist such organizations manage these extreme events more effectively. In this work, we argue that the mean Average Precision (mAP) metric that is commonly used to evaluate typical object detection algorithms can not be trusted for the fire detection task, due to its high dependence on the employed data annotation strategy. This means that the mAP score of a fire detection algorithm may be low even when it predicts fire bounding boxes that accurately enclose the depicted fires. In this direction, a new evaluation metric for fire detection is proposed, denoted as Image-level mean Average Precision (ImAP), which reduces the dependence on the bounding box annotation strategy by rewarding/penalizing bounding box predictions on image level, rather than on bounding box level. Experiments using different object detection algorithms have shown that the proposed ImAP metric reveals the true fire detection capabilities of the tested algorithms more effectively.