![]() And the application in wildfire location demonstrates that deep learning models trained on our dataset can be used in recognizing and monitoring forest fires. Extensive performance evaluations based on classical methods show that most of the models trained on FASDD can achieve satisfactory fire detection results, and especially YOLOv5x achieves nearly 80 % accuracy on heterogeneous images spanning two domains of computer vision and remote sensing. Deep learning models trained on FASDD demonstrate the potential value and challenges of our dataset in fire detection and localization. Meanwhile, out-of-the-box annotations are published in four different formats for training deep learning models. Additionally, we formulate a unified workflow for preprocessing, annotation and quality control of fire samples. It provides a challenging benchmark to drive the continuous evolution of fire detection models. To the best of our knowledge, FASDD is currently the most versatile and comprehensive dataset for fire detection. This work constructs a 100,000-level Flame and Smoke Detection Dataset (FASDD) based on multi-source heterogeneous flame and smoke images. A large-scale fire detection benchmark dataset covering complex and varied fire scenarios is urgently needed. The performance and generalization of fire detection models, however, are restricted by the limited number and modality of fire detection training datasets. Deep learning methods driven by in situ video and remote sensing images have been used in fire detection.
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