Abstract: ‍ ‍This paper presents an improved method for detecting small targets on the sea surface， aiming to overcome the performance limitations of statistical-based approaches in complex sea environments. The detection problem is transformed into a classification problem in a feature space by extracting features from both sea clutter and target echoes. Due to the limited number of target samples on the sea surface， causing sample imbalance， this paper proposes a target data augmentation model based on Wasserstein generative adversarial network with gradient penalty （WGAN-GP）. This model balances the target and sea clutter samples. The phase loss is designed to improve the overall loss function of the WGAN-GP， encouraging the generated samples to accurately learn and reproduce the phase characteristics exhibited by real data. Then， the high-dimensional features of the target and sea clutter are extracted and trained using a convolutional neural network （CNN）. Next， because it is difficult to control the false alarm rate in a high-dimensional feature space， the CNN is improved by setting the threshold of the Softmax classifier， and the false alarm probability is controlled. Finally， experimental validation is performed with the assistance of the publicly available IPIX radar dataset. The WGAN-GP-CNN detector proposed in this paper achieves an average detection probability of 0.8683 at an accumulation time of 1.024 s and a false alarm probability of 0.001， which provides good detection results.