## 4 Experimental Setup
### Quantitative Results  
For a comprehensive evaluation, we compared UMFNet against multiple state-of-the-art detectors. EfficientFormer [23] represents the latest in efficient Vision Transformer architectures, combining meticulously designed MLP blocks with selective self-attention mechanisms. Its lightweight design (9.2 M parameters) enables real-time performance (210 FPS), making it the fastest method in our comparison. However, this efficiency comes with a trade-off in accuracy (67.2% mAP@50), particularly in regions with complex light-shadow boundaries. Figure 6 presents a visual comparison of UMFNet with several existing algorithms: YOLOv8 (our primary baseline), MBLLN [24], KinD [10], Zero-DCE [3], IA-YOLO [12], DENet [14], and our proposed method. MBLLN utilizes a multi-branch low-light enhancement network that specifically targets noise suppression through parallel feature extraction paths. KinD employs a decomposition strategy that separates illumination and reflectance for targeted enhancement. Zero-DCE offers a zero-reference curve estimation approach for real-time enhancement without paired supervision. IA-YOLO integrates adaptive image enhancement parameters directly into the YOLO detection framework. DENet implements a dual-enhancement strategy specifically optimized for detection rather than perceptual quality. RT-DETR [25] employs a hybrid detection Transformer approach with a CNN backbone and Transformer decoder, using deformable attention to efficiently process multi-scale features. Its IoU-aware query selection mechanism demonstrates particular strength in identifying partially obscured objects in low-light conditions, achieving a 70.7% mAP@50. However, its computational requirements (76.3M parameters and 140.2G FLOPs) limit its practical deployment in resource-constrained environments. In the field of low-light object detection, Table 2 reveals the performance evolution laws of three typical technical paths: (1) isolated enhancement strategies (such as Zero-DCE and KinD), which independently optimize the image quality with prior optical models, resulting in limited detection accuracy (mAP\({}_{50}\leq\) 68.7%); (2) joint optimization methods (such as DENet and MAET), which integrate enhancement and detection objectives through multi-stage joint training to improve  
Figure 5: Statistical characteristics of images at different illuminance levels in the ExDark dataset. (**a**) Pixel intensity histograms of images at different illuminance levels; (**b**) estimated signal-to-noise ratios at different illuminance levels; (**c**) edge response strengths at different illuminance levels; (**d**) scatter plot of the relationship between noise and illuminance in sample images. These statistical characteristics are consistent with the low-light imaging characteristics expected by the physical lighting model, verifying the rationality of the illuminance annotations in the dataset. the target semantic awareness (mAP\({}_{50}=69.5\%\)), but are affected by parameter redundancy and have a reduced inference speed (FPS \(<90\)); (3) the proposed UMFNet in this work, based on a frequency-domain adaptive fusion framework, which constructs an end-to-end low-light enhancement-detection collaborative network, achieving the best performance with a 73.1% mAP\({}_{50}\), with a significant improvement compared to previous methods. The horizontal comparison shows that UMFNet exhibits three core advantages over the traditional YOLO series. Cross-band perception stability: Compared with the classic YOLOv8 (mAP\({}_{50}=68.7\%\)), UMFNet has a gain of 37.1% in the key noise suppression index (NR@HF), effectively avoiding an increase in the false positive rate caused by high-frequency artifacts (FP\({}_{\rm HF}\downarrow 23\%\)). Model efficiency optimization: With a similar parameter count (43.9 M) to the baseline YOLOv8 (+0.7% parameters), the inference speed (FPS \(=150\)) is significantly better than those of two-stage enhancement models (such as LLFlow with FPS \(=122\)).