State Key Laboratory of Chemical Engineering, Tianjin Key Laboratory of Membrane Science and Desalination Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2022 Dec 7;22(24):9600. doi: 10.3390/s22249600.
Thanks to the advantages of low disturbance, good concealment and high mobility, bionic fishes have been developed by many countries as equipment for underwater observation and data collection. However, differentiating between true and bionic fishes has become a challenging task. Commonly used acoustic and optical technologies have difficulty in differentiating bionic fishes from real ones due to their high similarity in shape, size, and camouflage ability. To solve this problem, this paper proposes a novel idea for bionic fish recognition based on blue-green light reflection, which is a powerful observation technique for underwater object detection. Blue-green light has good penetration under water and thus can be used as a signal carrier to recognize bionic fishes of different surface materials. Three types of surface materials representing bionic fishes, namely titanium alloy, carbon fiber, and nylon, are investigated in this paper. We collected 1620 groups of blue-green light reflection data of these three kinds of materials and for two real fishes. Following this, three machine learning algorithms were utilized for recognition among them. The recognition accuracy can reach up to about 92.22%, which demonstrates the satisfactory performance of our method. To the best of our knowledge, this is the first work to investigate bionic fish recognition from the perspective of surface material difference using blue-green light reflection.
得益于低干扰、良好的隐蔽性和高机动性等优势，仿生鱼已被许多国家开发为水下观察和数据收集设备。然而，区分真假仿生鱼已成为一项具有挑战性的任务。由于形状、大小和伪装能力高度相似，常用的声学和光学技术难以区分仿生鱼和真鱼。为了解决这个问题，本文提出了一种基于蓝绿光反射的仿生鱼识别新方法，这是一种用于水下目标探测的强大观察技术。蓝绿光在水下具有良好的穿透性，因此可用作信号载体来识别不同表面材料的仿生鱼。本文研究了代表仿生鱼的三种表面材料，即钛合金、碳纤维和尼龙。我们收集了这三种材料和两种真鱼的 1620 组蓝绿光反射数据。随后，我们利用三种机器学习算法对它们进行了识别。识别准确率可达到 92.22%左右，这表明我们的方法具有令人满意的性能。据我们所知，这是首次从表面材料差异的角度利用蓝绿光反射来研究仿生鱼识别。
