Asymmetric Frequency-Decoupled Network for Robust Visible–Infrared Fire Detection

Wildfires are highly destructive natural disasters posing serious threats to ecosystems. Visible–infrared fusion is an effective paradigm for robust fire detection in complex scenarios. However, existing spatial-domain fusion methods inevitably suffer from cross-modal contamination: the strong thermal radiation of flames dilutes visible textures, while dense visible smoke suppresses infrared targets. To circumvent this bottleneck, we reveal the frequency-domain asymmetry of fire features and propose an Asymmetric Frequency-Decoupled Network (AFDNet). By shifting from symmetric spatial fusion to asymmetric frequency decoupling, AFDNet effectively isolates modal conflicts, enabling targeted feature enhancement without mutual interference. Specifically, a Modality-Specific Frequency Decoupling (MFD) module first employs the Discrete Wavelet Transform to decompose features into low- and high-frequency sub-bands, breaking spatial entanglement. Subsequently, for low-frequency energy, a Thermal-Guided Low-Frequency Aggregation (TLA) module leverages infrared local contrast as a physical prior to guide fusion, ensuring precise thermal localization while preserving visible scene semantics. For high-frequency details, a Smoke-Masked High-Frequency Restoration (SHR) module maps smoke-induced visible high-frequency collapse into a soft reliability gate. This gate introduces infrared details to compensate for smoke-weakened structural cues. Extensive experiments on the RGBT-3M dataset demonstrate that AFDNet achieves state-of-the-art performance, outperforming the second-best method by 3.37% in mAP, while exhibiting exceptional robustness in complex wildfire environments.