Smoke 100k: A Database for Smoke Detection

Due to the complex scenarios and the limited feature information in a single image, a precise smoke detection is much more challenging in practice. Most of previous smoke detection methods either extract textural and spatiotemporal characteristics of smoke or separate the smoke and background components of the image. However, those methods often fail in detecting smoke positions because of the limited feature information within a single image. Moreover, the task of smoke detection can be better achieved if the extra information from collected training dataset is available. One key issue is how to build a training dataset of paired smoke images and ground-truth bounding box positions for end-to-end learning. This paper proposes a large-scale benchmark image dataset to train a smoke detector. With the built dataset, experimental results demonstrate that the discriminative models can be effectively trained as the smoke detector to detect the smoldering fires precisely.