To solve the problem of overfitting with limited labeled samples for surface object detection based on semi-supervised learning and enhance the effectiveness of extracting objects from unlabeled samples, a semi-supervised surface object detection algorithm based on multi-view cross-consistency learning is proposed. First, this algorithm generates different views for training samples through data augmentations to enrich the diversity of the dataset. Then a multi-view target discriminator is advanced to generate pseudo-labels online for the unlabeled samples, extracting useful information from unlabeled samples. Finally, the multi-view cross-consistency learning is implemented to achieve cross-consistency regularization between the outputs of different views of the same instance, prompting the detection model to learn discriminant features and mitigate the risk of overfitting. The experimental results at maritime and inland rivers show that the proposed algorithm improves the discrimination of feature extraction. The detection accuracy of multi-category surface objects reaches 90.1%, 18.7% higher than the full supervised detection algorithm and over 3.8% higher than other semi-supervised detection algorithms. Regarding detection speed, the algorithm reaches 13.1 frames per second, basically meeting the real-time requirements. The algorithm through multi-view cross-consistency learning tends to improve the discrimination of features and reduce the overfitting risk of the detection model, with the performance of semi-supervised surface object detection optimized.