为实现手术器械的准确分割，提出一种基于改进Swin Transformer的双编码网络手术器械分割方法，利用Swin Transformer和卷积神经网络（convolutional neural network, CNN）不同的编码优势，有效捕获图像特征的全局语义信息和局部细节信息，提高模型的表达能力。为尽可能弥补下采样过程中的特征细节损失，设计了一个多尺度分辨率特征金字塔池化（multi-resolution feature pyramid pooling, MFPP）模块，通过结合不同维度特征，获取更多尺度的上下文信息，增强局部细节信息表达。最后，在跳跃连接中增加一个坐标注意力模块，将目标位置信息与通道信息相融合，对手术器械目标进行精确感知。实验结果表明，所提方法在手术器械二值分割和部分分割中，均获得了更为精确的分割结果，进一步验证了所提方法的有效性和准确性。

In order to achieve accurate segmentation of surgical instruments, a dual-encoding network surgical instrument segmentation method was proposed based on improved Swin Transformer. By taking advantage of different coding advantages of Swin Transformer and convolutional neural network(CNN), the global semantic information and local details of image features can be effectively captured to improve the expression ability of the model. To compensate for the loss of feature details during the downsampling process as much as possible, the multi-resolution feature pyramid pooling(MFPP) block was constructed to obtain more scale context information by combining different dimensional features and enhance the expression of local detail information. Finally, a coordinate attention block was added in the skip connection to fuse target position information with feature information for precise perception of the surgical instrument targets. The experimental results show that the proposed method achieves more accurate segmentation results in both binary and parts segmentation of surgical instruments, further verifying the effectiveness and accuracy of the proposed method.