For the interaction between marine robots and divers in the underwater environment, a method of diver’s gesture recognition and segmentation is proposed. This method first uses the progressive growing training method to optimize the generative adversarial networks, generating high-resolution images with complex content. Then, we use the generative adversarial network model as a data augmentation method and generate high-resolution images. We make the masks of gestures in the new dataset and use the mask R-CNN algorithm for gesture recognition and gesture segmentation. The experimental results show that the generating data improves the accuracy of several object recognition algorithms but cannot completely replace the original data and the mean average precision of gesture recognition is 0.85. The visualization shows the validity and weakness of segmentation.

针对海洋机器人与潜水员在水下环境中的交互，提出了一种潜水员手势识别与分割方法。该方法首先使用渐进式增长训练方法来优化生成对抗网络，生成内容复杂的高分辨率图像。然后，我们使用生成对抗网络模型作为数据增强方法并生成高分辨率图像。我们在新数据集中制作手势掩码，并使用掩码 R-CNN 算法进行手势识别和手势分割。实验结果表明，生成的数据提高了几种物体识别算法的精度，但不能完全替代原始数据，手势识别的平均精度为0.85。