Automated segmentation of retinal vessels is crucial for the auxiliary diagnosis and treatment of various ophthalmic diseases. To address the challenges of vessel loss, vessel breakage, and background miss-segmentation into vessels in retinal vessel segmentation, we propose a method that combines multi-directional stripe convolution and pyramid dual pooling. First, the four-direction stripe convolution module is used to enhance vessel feature extraction, where four directions refer to horizontal, vertical, antidiagonal, and main diagonal directions. Second, the pyramid dual pooling feature fusion module extracts features via average pooling and max pooling at multiple scales. Then, these obtained multi-scale features are fused to make the model understand and utilize local detail and global context more comprehensively. Finally, the incorporation of a channel-space dual attention module into the skip connection improves the focus of the model on critical features. Experimental results on the CHASE-DB1 and DRIVE datasets demonstrate that the proposed method outperforms existing mainstream segmentation methods in terms of the area under the receiver operating characteristic curve and accuracy evaluation metrics, indicating its potential to assist in the clinical diagnosis of relevant ophthalmic diseases.