针对传统船只目标检测算法的精度较低、船只检测类别单一等问题，提出一种改进的DGA-YOLOv8海上船只目标检测算法。首先，网络采用可变形卷积扩大模型的感受野，通过引入可学习偏移量，使模型能够根据物体实际形状自适应调整感受野大小和形状，确保卷积区域能够精确覆盖船只物体的轮廓。其次，引入GAM（global attention mechanism）注意力机制，使网络能够有效突出船只目标的关键特征，提高目标识别能力。实验结果表明：改进后算法的精确度和平均精度均值（mAP）达到96.4%和92.2%，FPS（frame per second）为43.55，在提升精度的同时也保证了一定的检测速度，满足了实时性检测的需求。同时与其他主流算法对比，其中包括Faster R-CNN（faster region-based convolutional neural network）、YOLOv5s和YOLOv10等。结果表明：所提算法具有更高的平均精度和更显著的分类效果。

An improved DGA-YOLOv8 offshore ship target detection algorithm was proposed to tackle the issues of low accuracy and single ship detection categories that are present in traditional ship target detection algorithms. Firstly, the network was adapted to include deformable convolution, which expanded the model's receptive field. Learnable offsets were introduced, allowing the model to adaptively adjust the size and shape of the receptive field in response to the actual shape of the object, ensuring that the convolution area can precisely cover the contour of the ship object. Secondly, the incorporation of a GAM(global attention mechanism) attention mechanism enabled the network to effectively emphasize the key features of ship targets, thereby enhancing the target recognition capability. The experimental results demonstrate that the improved algorithm achieves accuracy and average accuracy mean (mAP) of 96.4% and 92.2%, respectively. An frames per second(FPS) of 43.55 is recorded, indicating not only an enhancement in accuracy but also the maintenance of a certain detection speed, thus fulfilling the requirements for real-time detection. When compared with other mainstream algorithms, such as faster region-based convolutional neural network(Faster R-CNN) and YOLOv5s, YOLOv10. The results show that the proposed algorithm exhibits higher average accuracy and significant superior classification performance.