To address the challenges of large parameter sizes and computational demands in existing ship detection algorithms, as well as the fluctuations in detection results caused by scale and perspective variations, we propose an improved lightweight inland ship detection algorithm, YOLO-LISD, based on YOLOv8n. First, an efficient feature-sharing detection head, incorporating detail-enhanced convolution, is introduced to replace the original detection head, improving detection consistency. Second, a slim-neck method is incorporated to optimize the neck network, reducing the model size while maintaining detection performance. Third, a global channel-adaptive magnitude-based pruning algorithm is proposed for depth compression, enhancing detection efficiency. Finally, a feature knowledge distillation approach, leveraging spatial and channel correlations, is designed to improve the detection accuracy of the pruned model. Experimental results demonstrate that, compared to YOLOv8n, YOLO-LISD reduces the number of parameters and computational complexity by 68.4% and 56.8%, respectively, while improving detection accuracy and mAP50:95 on the SeaShips dataset by 1.1% and 2.1%, respectively. In practical applications, the detection speed of low computing power equipment reaches 55 fps, meeting real-time requirements. Compared to other algorithms, it demonstrates significant advantages, validating the superiority of the proposed method.