Low-light object detection is a major challenge in object detection tasks. Conventional methods for object detection exhibit significant performance degradation under low-light conditions, and existing low-light object detection methods consume excessive computational resources, making them unsuitable for deployment on devices with limited computing capabilities. To address these issues, this study proposes an end-to-end lightweight object detection algorithm called low-light YOLO (LL-YOLO). To tackle the problem of unclear and difficult-to-learn features in low-light images, a low-light image generation algorithm is designed to generate low-light images for training the detector, assisting it in learning feature information in low-light environments. In addition, the network structure of the detector is adjusted to reduce the loss of feature information during computation, thereby enhancing the model's sensitivity to feature information. Furthermore, to mitigate the problem of severe noise interference on feature information in low-light images, an aggregation ELAN (A-ELAN) module for aggregating peripheral information is proposed that uses depth-wise separable convolution and attention mechanisms to capture contextual information, enhance the obtained feature information, and weaken the impact of noise. Experimental results demonstrate that the LL-YOLO algorithm achieves a mAP@0.5 of 81.1% on the low-light object detection dataset ExDark, which is an improvement of 11.9 percentage points over that of the directly trained YOLOv7-tiny algorithm. The LL-YOLO algorithm exhibits strong competitiveness against existing algorithms.