Work

Real-time Uav Early Smoke Detection With a Lightweight, Feature-refined Detector

Year: 2026

Type: article

Abstract: Wildfires are highly destructive, making early smoke detection crucial for rapid suppression. Existing UAV-based methods face challenges such as slow inference, limited accuracy, and weak sensitivity to small or diffuse smoke. This study introduces a lightweight YOLOv8-based detector enhanced with (1) Wise-IoU v3 for robust localization, (2) Ghost Shuffle Convolution for reduced computation and real-time efficiency, and (3) BiFormer attention to emphasize smoke cues while suppressing clutter. On a UAV smoke dataset, the model achieves 79.4% AP (+3.3 over baseline), with notable performance on small (71.3%) and large (92.6%) targets. The results highlight improved accuracy and efficiency, supporting practical real-time UAV-based wildfire monitoring.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19642213

Authors: Saydirasulov, Norkobil, Kilichev, Dusmurod

Institution: Gachon University

Language: English

Topic: Fire Detection and Safety Systems

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7154850158

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