A lightweight attention mechanism and cross-scale fusion based ship target detection algorithm is proposed to address the issues of slow detection speed and low detection rates caused by limited computing power resources onboard. Based on YOLOv5s algorithm, a lightweight attention mechanism of SimAM is introduced into the backbone network and fused cross-scale with the neck network, thereby improving the detection accuracy of the algorithm. Lightweight convolutions of C3Ghost and GhostConv are incorporated to reduce the parameters of the detection algorithm, enabling real-time ship detection. For bounding box regression loss, adaptive parameters are employed to enhance the adaptability and robustness of anchor box. Finally, comparative and ablation experiments with mainstream algorithms are conducted on the SeaShips dataset. The experimental results validate the effectiveness of the proposed algorithm.