To address the issues of inferior image quality, uneven lighting, and blurred details in low-light environments that result in low detection accuracy, this study proposes a night-time detection model named LowLight-YOLOv8n, which is an improved version of YOLOv8n. First, a low-light image enhancement network named Retinexformer is introduced before convolutional feature extraction in the Backbone network, thus improving the visibility and contrast of low-light images. Second, conventional convolution operations are replaced with RFCAConv in both Backbone and Neck networks, where convolution kernel weights are adjusted adaptively to solve the issue of shared parameters in conventional convolutions, thus further enhancing the model's feature extraction and downsampling capabilities. Subsequently, a new C2f_UniRepLKNetBlock structure is formed by combining the large convolution kernel architecture of UniRepLKNet with the C2f module of the Neck network, thereby achieving a larger receptive field that encompasses more areas of the image with fewer convolution operations, thus allowing a broader range of contextual information to be aggregated, and more potential target information to be captured in low-light images. Finally, a new bounding-box regression loss function named Focaler-CIoU is adopted, which focuses on the detection of difficult samples. Experimental results on the ExDark dataset show that, compared with the baseline model YOLOv8n, LowLight-YOLOv8n improves the mAP@0.5 and mAP@0.5∶0.95 metrics by 6.8% and 5.8%, respectively, and reduces the number of parameters by 0.09×10⁶.