# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 1 Introduction
While effective, the sequential integration of modules may lead to suboptimal information flow, as each component (enhancement, attention, detection) operates in isolation rather than dynamically reinforcing one another. [PERSON] et al. [34] introduced YOLO_GD with cross-layer fusion and dual-layer spatial attention, mitigating inter-layer information loss. However, the spatial-only attention mechanism struggles to capture long-range dependencies across channels, which are essential for reconstructing color and texture in low-light images where channel correlations weaken. [PERSON] et al. [35] enhanced YOLOv7 with agile hybrid convolutions and deformable attention, improving edge extraction. Yet, the lack of a unified framework to balance local detail extraction (e.g., edges) and global context (e.g., object layout) remains an issue amplified in low-light scenes where both types of information are degraded but interdependent. [PERSON] et al. [36] developed GOI-YOLO with group convolutions to isolate edge interference, enhancing efficiency. However, the exclusive focus on spatial feature grouping neglects the potential of channel-wise attention to recover low-light spectral information, such as restoring discriminative color features muted by insufficient illumination. [PERSON] et al. [5] proposed multi-stage image enhancement for anomaly detection in smart cities, reducing false positives. Nevertheless, the static enhancement pipeline does not adapt to diverse low-light sub-scenarios (e.g., near-infrared vs. visible light), limiting its generality across illumination conditions. Although the aforementioned studies have mitigated some of the challenges associated with object detection in low-light conditions, several unresolved issues persist: (1) Insufficient integration of spatial-local and channel-global information, (2) static feature extraction mechanisms ill-suited to low-light sparsity, and (3) insufficient emphasis on the importance of contextual information. However, existing methodologies in the realm of low-light object detection predominantly concentrate on enhancing the detection performance of individual objects, often neglecting the importance of contextual information. These methodologies primarily rely on image processing techniques, such as denoising, contrast enhancement, and edge detection, to improve the visibility and detectability of objects in low-light scenarios. Despite the advancements made in these techniques, the lack of emphasis on contextual information limits the overall performance and accuracy of low-light object detection systems. Secondly, the capacity to extract local detail features from images captured in low-illumination environments remains insufficient. Local detail features are crucial for accurately identifying and classifying objects within an image. However, in low-light conditions, the reduction in photon count and the increase in noise levels result in blurred and distorted images, making it difficult to extract reliable local detail features. To address these challenges, this paper, Dark-YOLO, addresses these by designing a unified framework where adaptive enhancement, dynamic feature selection, and reciprocal attention mechanisms enable collaborative optimization, ensuring robust performance under complex low-light constraints. The main contributions of this paper are as follows:  
(1) We introduce two complementary attention mechanisms. One is SimAM in the Partial Convolutional Spatial Module (PSM), which applies pixel-level focused attention to key features, enabling the model to prioritize critical local information. The other is the Dimensional Reciprocal Attention Module (D-RAMiT), which models global-local relationships through bidirectional spatial-channel attention, integrating long-range dependencies across spatial layouts and channel correlations. Together, they enhance the model's perception of contextual information in low-light scenarios by explicitly defining \"contextual information\" into two complementary dimensions: global context and local context.