This study addresses the challenges associated with high computational parameter volume, intricate complex background lighting, and the diversity of the ship's lights in the context of target detection. improvements have been made to YOLOv8n to meet the requirements for real-time and accurate identification of ship’s lights. Initially, we employ the VanillaNet as the core feature extraction network, which significantly diminishes the model's computational overhead and ensures compliance with real-time detection demands. Subsequently, we integrate a color attention module designed to discern and amplify the distinctive color attributes of the navigation lights, thereby bolstering their recognition even amidst complex lighting conditions. Furthermore, to accommodate the unique characteristics of the ship's lights, including their size, frequency, and spatial distribution across different perspectives, introduce a Mixture of Experts (MoE)-layer module as a substitute for the conventional C2f module, further refining the identification accuracy. Finally, the Focal Loss function is adopted to adjust the focus on easy-to-hard classified samples, addressing the class imbalance issue and improving the model's ability to detect ship’s lights. Experimental results demonstrate that, compared to the original baseline model YOLOv8n, the improved model reduces parameters and computation by 37.7% and 52.8%, respectively, while increasing accuracy and mAP@0.5 by 3.3% and 2.2% to 98.3% and 98.7%, respectively, and therefore the improved YOLOv8n meets the requirements for real-time identification of ship’s lights.