Faster R-CNN algorithm cannot achieve accurate ship detection. Therefore, a ship detection algorithm based on a deep feature pyramid and cascade detector is proposed in this study. First, the small-target data enhancement algorithm is used for expanding the data to ensure that sufficient features are learned by the detection model. Then, the deep feature pyramid network is used for improving the feature extraction network of the original target detection algorithm, suppressing the coherent speckle noise, and effectively extracting the ship features. Further, a cascading structure is adopted to adjust the improved network according to the sparse features of the ship targets obtained from the synthetic aperture radar (SAR) images. Based on the aforementioned improvements, some images from the ship target detection dataset and the SAR images of the Bohai Bay captured in February are selected for performing the experiments. Experimental results show that, the proposed algorithm achieves good detection results, proving its effectiveness with respect to ship detection.