为了解决船舶目标检测算法参数量与计算量大，以及内河环境下近岸复杂背景影响和船舶相互遮挡导致船舶检测困难的问题，基于YOLOv7-tiny做出改进，提出MED-YOLO船舶目标检测轻量化算法。首先，使用MobileNetV3网络作为主干特征提取网络，极大地降低了模型计算成本；其次，将EMA注意力模块引入颈部网络，构建EMA-ELAN模块，增强网络多维度感知和多尺度特征提取能力；然后，选用将尺度感知、空间感知和任务感知三合一的Dyhead作为改进模型的检测头部，以获得更强的特征表达能力；最后，使用具有动态非单调聚焦机制的WIoU作为模型边界框损失函数，提高模型应对船舶遮挡情况的能力，提升检测性能。实验结果表明，MED-YOLO相较于YOLOv7-tiny在参数量与计算量方面分别减少了39.8%和55.0%，精度与mAP@0.5分别提高了1.4%和1.0%，达到了98.3%和98.9%，在实现轻量化的同时具有更好的检测性能，满足了计算资源受限环境下的部署需求，具有一定的工程实际意义。

To solve the problem of the large number of parameters and computation of ship target detection algorithm, as well as the difficulties of ship detection caused by the influence of the nearshore complex backgrounds and the mutual occlusion of ships in inland river environments, this paper makes improvements based on YOLOv7-tiny and proposes a lightweight algorithm MED-YOLO for ship target detection. Firstly, the MobileNetV3 network is used as the backbone feature extraction network, which greatly reduces the calculation cost of the model. Secondly, EMA attention module was introduced into the neck network, and EMA-ELAN module was constructed to enhance the multi-dimensional perception and multi-scale feature extraction capability of the network. Then, Dyhead, which combines scale perception, spatial perception, and task perception, is selected as the detection head of the improved model to obtain stronger feature expression ability. Finally, WIoU with dynamic non-monotonic focusing mechanism is used as the bounding box loss function to improve the model's ability to cope with ship occlusion and improve the detection performance. The experimental results show that compared with YOLOv7-tiny, MED-YOLO has 39.8% fewer parameters and 55.0% less computation, and its precision and mAP@0.5 have increased by 1.4% and 1.0% respectively, reaching 98.3% and 98.9%, which not only achieves lightweight, but also has better detection performance. It meets the deployment requirements in the environment with limited computing resources, and has certain practical engineering significance.