To solve the problem of small target ship detection in synthetic aperture radar (SAR) image, a feature enhanced small target detection algorithm based on the single shot multi box detector is proposed. The algorithm proposes a hybrid feature extraction module, which uses general convolution, dilated convolution with different dilation ratio and asymmetric convolution to form a receptive field matching the ship target, so as to improve the feature extraction ability of shallow network for small targets with complex shape. Then, a neighbor feature fusion module is proposed, which integrates the feature information more scientifically and deeply, and further enhances the features of small targets. Finally, according to the characteristics of the single channel of the SAR image, redundant feature channels of the feature extraction network VGG-16 are reduced. It is compared with other detection algorithms on the public SSDD data set. The experimental results show that the proposed method improves the average accuracy to 93.44$%$, the detection speed is improved to 41.8FPS, and the number of parameters is reduced to 18.74M, which is superior to other detection algorithms in comprehensive performance.