# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 1 Introduction
The global context is captured by D-RAMiT's Spatial Self-Attention (SPSA), which encodes the overall image layout (such as the relative positions of objects) to understand scene-level structure and spatial dependencies. The local context is preserved through the cross-overlapping pooling of the Cross-Spatial Pyramid Pooling Feature (CSPPF) and the partial convolution of PSM. These operations mitigate noise and retain spatial hierarchies, thus preserving fine-grained details like edges and textures that degrade under low-light conditions. SimAM dynamically ranks the importance of local features to focus on discriminative pixels, while D-RAMiT constructs global contextual representations by modeling the interdependencies between spatial locations and channel dimensions. The synergy of the two forms a \"multi-attention framework\" that balances the prioritization of local details and the modeling of global context, enabling robust feature learning in challenging low-light environments. (2) The proposed method introduces SCINet, an image enhancement module that cascades multiple illumination learning blocks. Each block is designed to learn and correct specific aspects of the image's illumination. This cascading structure enables the network to progressively refine illumination information, restoring finer details in low-light images and enriching feature representation. (3) An enhanced spatial feature pyramid module, termed CSPPF, is proposed. This module utilizes cross-overlapping average pooling and max pooling to minimize the loss of local information in the image, thereby improving the retention of critical spatial details. Max pooling captures salient edges (robust to noise), while average pooling retains global brightness gradients; their combination mitigates the detail loss caused by traditional single pooling in low-contrast images. (4) A dynamic feature extraction module, PSM, is introduced. By integrating selective information from partial convolution with a parameter-free attention mechanism, PSM enhances the network's feature extraction capacity and strengthens its object localization capability. In low-light images, partial convolution (PConv) ignores invalid/noisy pixels in the receptive field, reducing interference from corrupted data, while SimAM directs attention to rare valid features. (5) The method employs Dimension Reciprocal Attention Module (D-RAMiT), a dimension reciprocal attention module, which computes self-attention in parallel along both spatial and channel dimensions. This establishes long-range relationships between pixels, ensuring comprehensive utilization of both local and global information, and ultimately boosting the network's object detection accuracy in low-light conditions. In dim scenes, local features are sparse and ambiguous; D-RAMiT's spatial self-attention (SPSA) recovers fine-grained details, while channel self-attention (CHSA) models inter-channel dependencies, enabling the model to infer object identities from limited local cues using global scene statistics. The remainder of this paper is organized as follows. In Section 2, the specific implementation details of Dark-YOLO will be presented. Subsequently, Section 3 will discuss the experimental results of Dark-YOLO, including a variety of ablation studies and comparisons with different algorithms on the ExDark and DarkFace datasets, highlighting the advantages and robustness of Dark-YOLO. Finally, Section 4 will provide the conclusion, summarizing Dark-YOLO, elucidating its strengths, and addressing its limitations along with potential improvements and future enhancements.