multimodal fusion
FLAME2-DT (Forest Fire Detection Dataset with Dual-modality Labels) is a comprehensive multi-modal dataset specifically designed for UAV-based forest fire detection research. The dataset consists of 1,280 paired RGB-thermal infrared images captured by a Mavic 2 Enterprise Advanced UAV system, with high-resolution (640×512) and precise pixel-level annotations for both fire and smoke regions. This dataset addresses critical challenges in forest fire detection by providing paired multi-modal data that captures the complementary characteristics of visible light and thermal imaging.
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The objective of this study is to conduct a systematic examination of research trends and hotspots in the domain of autonomous vehicles leveraging deep learning, through a bibliometric analysis. By scrutinizing research publications from various countries spanning 2017 to 2023, this paper aims to summarize effective research methodologies and identify potential innovative pathways to foster further advancements in AVs research. A total of 1,239 publications from the core collection of scientific networks were retrieved and utilized to construct a clustering network.
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