针对现有的目标检测模型在提取溢油区域复杂特征以及识别不规则形状溢油区域存在的局限性，提出一种改进后的Mask R-CNN模型，使其更好地适应海面溢油检测任务。首先，在该模型特征提取网络中引入可变形卷积模块，提升模型对不规则形状溢油区域的感知能力；其次，在模型中加入改进后的注意力机制，使模型在增强对溢油区域关键特征捕捉能力的同时增加较少的参数；最后，使用完全交并比损失函数(Complete Intersection over Union Loss，CIoU Loss)作为目标框回归损失函数，从而提升模型在边界框回归任务上的性能。在公开的合成孔径雷达(Synthetic Aperture Radar，SAR)海面溢油数据集上的实验表明，改进后的模型检测精度为66.14%，相比原始Mask R-CNN模型提高了5.33%。此外在同一数据集的基础上与目标检测模型Yolov9、Yolov10、Faster R-CNN以及Cascade R-CNN模型相比，精度分别提高了31.79%、 19.01%、30.47%和21.02%；与实例分割模型Yolact和Yolov5-seg模型相比，精度分别提高了22.94%和29.5%。

In light of the limitations of existing object detection models in extracting complex features of oil spill areas and identifying irregularly shaped oil spill regions, an improved Mask R-CNN model is proposed to better adapt to the task of oil spill detection on the sea surface. Firstly, a deformable convolution module is introduced into the feature extraction network of the model to enhance its perception of irregularly shaped oil spill areas. Secondly, an improved attention mechanism is incorporated into the model, which not only strengthens the model's ability to capture key features of oil spill areas but also adds relatively few parameters. Lastly, the Complete Intersection over Union Loss (CIoU Loss) function is utilized as the bounding box regression loss function, thereby improving the model's performance in the bounding box regression task. Experiments conducted on a publicly available Synthetic Aperture Radar (SAR) sea surface oil spill dataset demonstrate that the detection accuracy of the improved model is 66.14%, representing an increase of 5.33% compared to the original Mask R-CNN model. Moreover, on the same dataset, when compared with object detection models Yolov9, Yolov10, Faster R-CNN, and Cascade R-CNN, the accuracy is respectively enhanced by 31.79%, 19.01%, 30.47%, and 21.02%. In comparison with instance segmentation models Yolact and Yolov5-seg, the accuracy is respectively elevated by 22.94% and 29.5%.