## 1 Introduction  
Modern autonomous systems demand reliable object detection under extreme low-light conditions--a realm where even state-of-the-art detectors fall significantly short. As per the EXDAR benchmark [1], YOLOv8 suffers a catastrophic drop when the illumination falls below 5 lux. This performance drop stems from an unresolved **spectral trilemma**: (1) aggressive high-frequency (HF) denoising erodes critical edges, (2) low-frequency (LF) enhancement amplifies illumination bias, and (3) cross-band interference corrupts multi-scale feature hierarchies.  
Prior works navigate this minefield through two flawed paradigms.  
**Enhance-then-Detect (ED)** approaches [2, 3] apply frequency-blind enhancement before detection. Although computationally efficient, their fixed operations over-smooth HF details--DarkFace evaluations [4] reveal 11.2% lower edge accuracy versus daylight performance.  
**Joint Enhancement-Detection (JED)** frameworks [5] co-train enhancement and detection modules. However, without explicit frequency disentanglement, noisy LF componentspropagate unrestricted, limiting their AP@50 to 31.8% at \(\leq\)1 lux--which is inadequate for safety-critical applications.  
The core limitation is **static frequency processing**: existing methods employ rigid wavelet discrete cosine transform (DCT) bases that are incapable of adapting to dynamic noise distributions. As the illuminance decreases, the HF noise variance grows exponentially, while the LF signals shift nonlinearly due to Bayer demosaicing artifacts. Treating these intrinsically coupled phenomena as separate problems proves fundamentally inadequate. We challenge this status quo through **UMFNet--**the first architecture unifying three breakthroughs. **Frequency-Adaptive Fusion (FAF)**: We replace fixed filters with learnable convolutional wavelets that dynamically adjust the HF/LF thresholds via detection loss gradients. **Spatial-Channel Coordinated Attention (SCCA)**: We apply cross-band gates where LF semantics guide HF denoising. **Spectral Perception Loss**: We jointly optimize the frequency-weighted SSIM and detection confidence gradients, enforcing physics-informed feature recovery. Extensive validation confirms UMFNet's leadership: 73.1% mAP@50 on EXDAR (+3.4% over SOTA). Crucially, our frequency gradient alignment principle establishes a new theoretical foundation for vision systems in photon-starved environments. Furthermore, compared with the original YOLOv8, our method improves the mean average precision at a 50% intersection over union (mAP50) by approximately 6.4%. This indicates that the combination of UMFNet and YOLOv8 significantly enhances the detection accuracy under low-light conditions. The remainder of this paper is organized as follows. Section 2 reviews related work in low-light image enhancement and object detection. Section 3 presents our proposed UMFNet method in detail. Section 4 describes the experimental setup and results. Finally, Section 5 concludes our work and suggests future research directions. Based on the limitations identified in prior works, we establish three explicit research objectives.  
* **Objective 1:** To develop a frequency-adaptive fusion mechanism that dynamically adjusts the high-/low-frequency thresholds based on the illumination conditions, addressing the static processing limitation in existing methods.
* **Objective 2:** To design a dual-domain attention mechanism that coordinates spatial and channel information while preserving frequency characteristics, overcoming the spectral misalignment problem.
* **Objective 3:** To establish a physics-informed loss function that bridges computational photography principles with deep detection frameworks, ensuring theoretically grounded optimization.