To improve the recognition accuracy of small maritime targets in remote sensing images, a YOLO algorithm integrating multi-kernel sub-pixel enhancement and contextual attention is proposed based on the YOLO11n object detection framework. This method simultaneously employs 3×3 and 9×9 convolutional kernels to extract features with different receptive fields and adopts a super-resolution reconstruction approach to design a sub-pixel enhancement module that captures more detailed information, thereby enhancing the feature extraction capability of the backbone network. On the basis of the original C3K2 structure, a C3K2_context aggregation attention (C3K2_CAA） module is designed to capture richer contextual information, assisting the model in more accurately recognizing targets and improving detection performance in complex scenarios. The network′s detection effectiveness is validated using the publicly available SeaShips dataset. The results show that double-core sub-pixel enhancement module (DSPE）-YOLO algorithm achieves AP, mAP50 and mAP50-95 of 97.2%, 99.5% and 71.6%, respectively. Compared with the baseline YOLO11n network, the recognition accuracy increases by 4.3%, 9.4% and 4.4%, respectively. For ship detection, the AP is improved by 0.4%, 7.7%, 4.7% and 8.7% compared with PP-YOLOE-s, YOLOV6, YOLOV8-n and YOLOV10-n demonstrating excellent object detection performance.