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Intelligent ship detection based on high-precision synthetic aperture radar (SAR) images plays a vital role in ocean monitoring and maritime management. Denoising is an effective preprocessing step for target detection. Morphological network-based denoising can effectively remove speckle noise, while the smoothing effect of which blurs the edges of the image and reduces the detection accuracy. The fusion of edge extraction and morphological network can improve detection accuracy by compensating for the lack of edge information caused by smoothing. This article proposes an end-to-end lightweight network called morphological feature-pyramid Yolo v4-tiny for SAR ship detection. First, a morphological network is introduced to preprocess the SAR images for speckle noise suppression and edge enhancement, providing spatial high-frequency information for target detection. Then, the original and preprocessed images are combined into the multichannel as an input for the convolution layer of the network. The feature pyramid fusion structure is used to extract the high-level semantic features and shallow detailed features from the image, improving the performance of multiscale target detection. Experiments on the public SAR ship detection dataset and AIR SARShip-1.0 show that the proposed method performs better than the other convolution neural network-based methods.