Although the data on a particular complicated sea conditions are available, it is difficult to collect the data on various complicated sea conditions simultaneously. When the sea conditions change, the test results of the detection network based on the data of simple sea conditions may be unacceptable. It is proposed that the VGG16, pix2pixHD, and cycleGAN methods should be applied to establish related datasets of different complicated sea conditions. After the unified image quality assessment indicators are determined, it is found that the images generated by the VGG16 network are most in line with the subsequent target detection standards. The real images of complicated sea conditions have the smallest root-mean-square error, the largest peak signal-to-noise ratio, and the best regression coefficient (R²). Meanwhile, the improved Faster R-CNN is introduced for target detection of small sample datasets. First, the ResNet50_FPN module, as the backbone feature extractor, is used to improve the detection performance of small-sized objects. Moreover, because there are many small target objects to be detected, the ROI Align module is preferred, for it is more accurate. Finally, the Softer-NMS algorithm is selected to significantly improve the positioning accuracy through confidence estimation. Compared with some previous Faster R-CNN methods, the accuracy and missed detection rate of the proposed method outperform other networks, with a mean average precision of 85.11%.