# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 1 Introduction
[23] optimized the backbone network of YOLOv5 and applied RetinexNet for data augmentation to improve occluded object detection, but RetinexNet's image enhancement performance required manual parameter adjustments to achieve optimal results. [PERSON] et al. [24] introduced an enhancement strategy combining block matching with three-dimensional filtering, effectively improving the signal-to-noise ratio in nighttime images. However, the approach did not fully address the limitations of convolutional neural networks in capturing global information. To address the aforementioned challenges, researchers have proposed a variety of solutions. For instance, [PERSON] et al. [25] introduced a framework called YOLA, which enhances object detection performance in low-light conditions by learning illumination-invariant features via the Lambertian image formation model. While this approach leverages inter-channel and spatial relationships to handle diverse lighting, it underemphasizes the integration of global contextual information, critical for resolving ambiguities in low-contrast scenes where object-background boundaries blur. [PERSON] et al. [26] developed the FeatEntHancer module, employing hierarchical multi-scale feature fusion guided by task-specific losses and multi-head attention. Although effective for cross-scale feature integration, this method relies on parameterized attention mechanisms that may overlook lightweight and dynamic feature selection tailored to low-light sparsity, potentially compromising efficiency in resource-constrained scenarios. [PERSON] et al. [27] proposed PE-YOLO, integrating a Pyramid Enhancement Network (PENet) with YOLOv3 to decompose images into Laplacian pyramid components for detail enhancement. However, the static pooling strategy in their feature pyramid risks losing fine-grained local details, especially problematic in low-light environments where texture and edge information are already degraded. [PERSON] et al. [28] introduced SDNIA-YOLO to enhance YOLO robustness under extreme weather via Neural Style Transfer (NST)-synthesized images. While this boosts generalization to adversarial conditions, it lacks a dedicated mechanism to address the unique challenge of low-light illumination, such as noise amplification and uneven brightness, which require more than just style adaptation. [PERSON] et al. [29] proposed IA-YOLO with a Differentiable Image Processing (DIP) module to adapt to varying weather. However, the independent operation of the DIP module and detection network may hinder seamless fusion of enhanced features, as demonstrated in [PERSON] et al. 's [21] earlier work, where parallel processing limited feature utilization efficiency. [PERSON] et al. [30] developed 3L-YOLO, a lightweight model that avoids explicit image enhancement by introducing switchable atrous convolution. While this reduces computational load, it overlooks the critical role of preprocessing in restoring lost low-light details--an omission that becomes pronounced in extremely dark environments with minimal initial information. [PERSON] et al. [31] presented D2 BGAN for unsupervised low-light-to-bright image conversion, effective against motion blur and noise in driving scenes. Yet, the separation of image enhancement and detection as distinct stages may not optimize end-to-end feature learning, limiting the model's ability to prioritize task-relevant details during enhancement. [PERSON] et al. [32] proposed NUDN for nighttime domain adaptation via knowledge distillation, reducing costs for small objects. However, this approach does not explicitly model the reciprocal relationship between spatial and channel-wise features--a key deficit in low-light scenarios where cross-dimensional dependencies (e.g., channel-wise contrast and spatial structure) are critical for accurate detection. [PERSON] et al. [33] improved YOLOv5 with DK_YOLOv5, incorporating enhancement algorithms and attention mechanisms.