论文标题
视觉潜水员的面部识别水下人体机器人相互作用
Visual Diver Face Recognition for Underwater Human-Robot Interaction
论文作者
论文摘要
本文为水下机器人提供了一种深入的面部识别方法，可以识别潜水员。具体而言，所提出的方法能够识别水下的潜水员，面孔被水肺口罩和呼吸器遮盖了。我们在这项研究中的贡献是在面部特征的严重遮挡和水下光学扭曲中的图像降解下对个体的强大识别。凭借正确认识潜水员的能力，自主的水下车辆（AUV）将能够与人类机器人团队中的正确人员进行协作任务，并确保只有被授权指挥机器人的人接受指导。我们证明，我们提出的框架能够通过不同的数据增强和发电技术从实际潜水员面孔学习歧视性特征。实验评估表明，该框架与最新的（SOTA）算法相比，预测准确性提高了3倍，非常适合嵌入机器人平台上的推断。
This paper presents a deep-learned facial recognition method for underwater robots to identify scuba divers. Specifically, the proposed method is able to recognize divers underwater with faces heavily obscured by scuba masks and breathing apparatus. Our contribution in this research is towards robust facial identification of individuals under significant occlusion of facial features and image degradation from underwater optical distortions. With the ability to correctly recognize divers, autonomous underwater vehicles (AUV) will be able to engage in collaborative tasks with the correct person in human-robot teams and ensure that instructions are accepted from only those authorized to command the robots. We demonstrate that our proposed framework is able to learn discriminative features from real-world diver faces through different data augmentation and generation techniques. Experimental evaluations show that this framework achieves a 3-fold increase in prediction accuracy compared to the state-of-the-art (SOTA) algorithms and is well-suited for embedded inference on robotic platforms.
