# 3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
## 1 Introduction  
Low-light object detection is a critical and fundamental task in vision, with a wide range of applications in areas such as security surveillance systems [1, 2], autonomous driving [3], agricultural production [4, 5], and other fields. Current research predominantly focuses on object detection in high-quality images under good lighting conditions, leading to the development of both one-stage [6, 7, 8, 9, 10, 11, 12] and two-stage methods [13, 14, 15]. In practical applications, poor lighting conditions are common, including low-light environments, nighttime, and underexposure. As a result of hardware limitations, the quality of the acquired images often deteriorates, leading to issues such as low contrast, high noise, and blurred boundaries. These factors weaken the geometric and texture features of the object, resulting in degraded object detection performance. Therefore, improving recognition capabilities under low-light conditions is crucial for advancing the practical applications of object detection. To address these challenges, researchers have adopted hardware-based approaches to improve object detection performance under low-light conditions. For example, [PERSON] et al. [16] employed thermal cameras for pedestrian detection and introduced a noveldetection network that integrates saliency maps derived from thermal images. Similarly, [PERSON] et al. [17] proposed a feature fusion framework that combines thermal and visible light data to improve detection performance in low-light conditions. While these methods enhance detection performance, they require costly data processing and specialized imaging equipment, limiting their widespread adoption. Therefore, high-performance low-light object detection algorithms have attracted significant attention from researchers recently. In low-light object detection tasks, most current methods decompose the problem into two stages: image enhancement and object detection. Image enhancement serves as a pre-processing step to improve the visibility and sharpness of images captured under low-light conditions before they are passed into a detector for object detection. Image enhancement techniques can be broadly classified into conventional methods and deep learning-based methods. Conventional image enhancement methods include histogram equalization [18], gamma correction [19], adaptive contrast enhancement [20], and Retinex [21]. In contrast, deep learning-based approaches [22; 23; 24] focus on end-to-end image enhancement. Object detection using deep learning-enhanced images has gained popularity due to its superior performance. For instance, [PERSON] et al. [25] designed a PWDN network for image enhancement, followed by using YOLOv8 for object detection. [PERSON] et al. [26] proposed an illumination adaptive transformer (IAT) network to enhance images, followed by YOLOv3 for object detection. [PERSON] et al. [27] designed a lightweight network to predict dynamic range and high-order curves for image enhancement, followed by the use of DSFD [28] for face detection, thereby addressing the challenge of face detection under low-light conditions. However, these image enhancement-based detection methods require substantial computational resources, making them challenging to deploy on devices with limited computing power. Furthermore, they are not easily trained end-to-end with object detection models. To reduce the complexity of two-stage low-light object detection, many researchers have focused on end-to-end networks. These architectures integrate image enhancement directly into the object detection network, improving overall system efficiency by simplifying the processing flow and reducing computational load. These methods provide an effective solution for object detection in low-light environments. For example, [PERSON] et al. [29] proposed a pyramid-enhanced network integrated with YOLOv3, creating an end-to-end dark object detection framework. Building on YOLOv8, [PERSON] et al.