Box2Pix: an automatic box-level to pixel-level annotation transformation method for fire detection in remote sensing images

Fire detection from remote sensing images plays a critical role in early fire warning, fire risk reduction, and post-fire damage assessment. Though deep learning-based approaches have achieved huge successes in fire detection and locating, their data-driven nature requires substantial human resources and time for meticulously labeling pixel-level annotations. The challenge of limited well-annotated labels is still hampering its application. In this paper, we propose a novel label evolution method, Box2Pix, that is to automatically transform box-level labels into pixel-levels labels, alleviating the heavy labor and time cost of pixel-wise fire annotation. Box2Pix leverages box-level annotated object detection datasets with the pre-trained large segmentation models to generate pixel-level annotated semantic segmentation datasets to further boost fire detection performance. Moreover, we introduce innovative point prompt generation methods by leveraging superpixel segmentation and saliency detection to identify potential target regions for generating random points, augmenting the semantic segmentation capability of Segment Anything Model (SAM). Box2Pix eliminates the need for extensive human involvement, avoids reliance on large-scale datasets for training or fine-tuning, enhancing the speed and quality of pixel-level label generation while reducing annotation difficulty and costs. The experiment on the highly generalized Flame And Smoke Detection Dataset (FASDD) demonstrates its superiority over the existing state-of-the-art methods, with improvements of 52.51% in mIoU and 41.38% in overall accuracy. Based on Box2Pix, the largest pixel-level fire detection dataset, Flame And Smoke Segmentation Dataset (FASSD) is generated. The newly created dataset is accessible on: https://github.com/OyamingO/Box2Pix.