Abstract: To accommodate the complex surface environment and resource-constrained unmanned surface vehicle (USV) platforms, and to meet the demand for high-precision and lightweight algorithms in surface target detection, this paper proposes an improved lightweight surface target detection algorithm, YOLO-WaterLite, based on YOLOv11n. First, the C3K2-SCG module is introduced into the backbone network to optimize the feature extraction process, further eliminating feature redundancy while enhancing the representation of both global and local features, thus maintaining the lightweight nature of the network. Second, a multi-scale feature aggregation-diffusion mechanism is designed to address the issue of multi-scale feature fusion, improving the network’s ability to integrate multi-scale contextual information and consequently enhancing detection accuracy. Finally, a joint task dynamic detection head is proposed, which enhances the interaction between classification and localization tasks through a shared feature extractor, significantly improving the model’s robustness and accuracy in detecting small surface targets. Experimental results show that YOLO-WaterLite achieves a 5.4% and 2.8% improvement in mAP at 0.5 over the baseline model YOLOv11n on the water surface object detection dataset (WSODD) and FloW-Img, respectively, with recall rates improving by 3.4% and 3.9%. Additionally, YOLO-WaterLite has only 2.4 M parameters, demonstrating significant advantages in both performance and efficiency compared to other mainstream lightweight detection algorithms.