EF-YOLO: Detecting Small Targets in Early-Stage Agricultural Fires via UAV-Based Remote Sensing
Jun Tao; Zhihan Wang; Jianqiu Wu; Yuan Li; Tomohiro Fukuda; Jiaxin Zhang · 2026 · Remote Sensing
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Early detection of agricultural fires with Unmanned Aerial Vehicles (UAVs) is important for environmental safety, yet it remains difficult because ignition cues are extremely small, smoke patterns vary widely, and farmland scenes often contain strong background interference such as specular reflections. Model development is further constrained by the scarcity of data from the early ignition stage. To address these challenges, we propose a joint data and model optimization framework. We first build a hybrid dataset through an ROI-guided synthesis pipeline, in which latent diffusion models are u
Abstract by Jun Tao; Zhihan Wang; Jianqiu Wu; Yuan Li; Tomohiro Fukuda; Jiaxin Zhang, Remote Sensing (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/rs18081119