# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 1 Introduction  
In the expansive field of computer vision, object detection has emerged as a cornerstone research focus, achieving remarkable progress in recent years and garnering significant attention. This technology has found widespread applications across numerous domains, including vehicle-assisted driving [1, 2], security surveillance [3, 4], pedestrian detection [5, 6, 7], industrial quality control [8], and assistive technologies for the visually impaired [9]. The practical deployment of object detection systems is heavily influenced by the quality of illumination in their operating environments. Under optimal lighting conditions--characterized by an abundance of photons and uniform illumination distribution--object features become distinctly discernible. This clarity enables a clear differentiation between the foreground (objects of interest) and the background, thereby providing favorable conditions for accurate object detection tasks. However, in low-light scenarios, such as nighttime or dimly lit environments, the photon count is significantly reduced, resulting in diminished contrast and inadequate brightness, which complicatesthe identification of objects and facial features [10]. Furthermore, the increased presence of noise and interference in low-light conditions exacerbates the challenges faced by object detection systems. These factors undermine the performance of existing object detection algorithms, rendering them less capable of consistently and accurately identifying objects, often failing to meet expected levels of detection accuracy and reliability. This limitation poses a substantial barrier to advancements in critical fields such as security surveillance, autonomous driving, and intelligent transportation systems [11,12]. Consequently, research into object detection under low-light conditions is of paramount importance, as it addresses these challenges and further propels the development of this technology. By developing robust and accurate object detection methods that perform effectively in low-light environments, the scope of this technology's applications can be broadened, contributing significantly to domains that rely on dependable object detection. In the field of low-light object detection, deep learning methods can be divided into the following two categories based on whether candidate boxes are generated: two-stage algorithms and one-stage algorithms. Two-stage algorithms, such as Faster R-CNN (Faster Region Convolutional Network) [13], first generate potential regions and then refine recognition. Although they achieve high accuracy, their processing speed is limited. [PERSON] et al. [14] proposed an object detection algorithm for nighttime environments that combines DCGAN with Faster R-CNN, incorporating a feature fusion module and a multi-scale pooling module to enhance detection performance under low-light conditions. [PERSON] et al. [15] combined deformable convolutional networks with Faster R-CNN, employing perceptual boundary localization to achieve more accurate position learning of objects, thereby improving detection performance in dim environments. One-stage algorithms, such as SSD [16] and the YOLO series [17, 18, 19, 20], perform detection directly, omitting the candidate box generation step. This leads to faster detection but demands a more comprehensive feature extraction process. [PERSON] et al. [21] proposed the DE-YOLO framework, which integrates DENet and YOLOv3 into a unified enhancement-detection system. However, the independent processing by the two networks in the framework may prevent the full utilization of enhanced image details during feature extraction. [PERSON] et al. [22] proposed a self-supervised method for low-light object detection using a multi-task autoencoder transformation model. Nevertheless, the accuracy of self-supervised learning can be compromised when sufficient contextual information is unavailable, particularly in low-light environments where the absence of contextual details further affects detection accuracy. [PERSON] et al.