基于卷积神经网络的水下湍流探测技术  

Underwater Turbulence Detection Technology Based on Convolutional Neural Networks

作　　者：贺锋涛 吴倩倩 张建磊 杨祎 张娟 姚欣钰 赵伟琳 HE Fengtao;WU Qianqian;ZHANG Jianlei;YANG Yi;ZHANG Juan;YAO Xinyu;ZHAO Weilin(School of Electronic Engineering,Xi′an University of Posts and Telecommunications,Xi′an 710072,China)

机构地区：[1]西安邮电大学电子工程学院,西安710072

出　　处：《光子学报》2024年第1期91-102,共12页Acta Photonica Sinica

基　　金：装备预研教育部联合基金(No.8091B032130);陕西省技术创新引导专项基金(No.2020TG‒001)。

摘　　要：针对水下湍流的复杂性和多变性对水下航行器性能和姿态控制产生的挑战,提出使用卷积神经网络来测量水下湍流的温差耗散率XT。首先,采用功率谱反演法和惠更斯-菲涅尔原理仿真生成了受水下湍流影响的散斑图像数据集。随后,利用卷积神经网络提取这些受湍流影响的散斑图像中的特征信息,并对温差耗散率XT进行估计。最后,通过现场实验数据集验证了所提出方法的可行性。实验结果表明,所提出的神经网络在实地实验数据集和模拟仿真数据集上表现出相似的分类精度和损失曲线,其测量准确率分别为98.8%和99.2%。这一研究为水下环境监测和资源勘探领域提供了重要的参考,对于光学图像处理和湍流研究等相关领域具有实际意义。Underwater vehicles have broad application potential in underwater environments,such as marine resource exploration,seabed geological survey,underwater operations,etc.However,underwater turbulence has a serious impact on the navigation of underwater vehicles,leading to a decrease in their ability to perceive and locate the surrounding environment,thereby affecting their task execution and performance.Underwater turbulence can cause attitude disturbances of underwater vehicles.The turbulent vortices and intense eddies cause significant changes in the flow velocity and direction,resulting in irregular thrust and resistance on the drone,making it difficult to control its attitude changes.This can lead to unstable attitude of drones,increasing the risk and difficulty of navigation.Turbulence has a negative impact on the perception and positioning ability of underwater vehicles.Due to the complexity and unpredictability of turbulence,it can cause optical speckle phenomena in underwater environments,making the images perceived by drones blurry and distorted.This leads to a decrease in the perception ability of drones to the surrounding environment,making it difficult to accurately identify and locate target objects,thereby affecting the accuracy and efficiency of task execution.In summary,the detection of underwater turbulence is crucial for the navigation decision-making and performance of underwater vehicles.By accurately detecting turbulence and making corresponding adjustments based on turbulence information,underwater vehicles can improve their navigation ability and stability in turbulent environments,thereby better adapting to complex underwater work tasks and environmental requirements.Therefore,this study proposes an innovative method for detecting underwater turbulence based on Convolutional Neural Networks(CNN)to measure the temperature difference dissipation rate of underwater turbulence.The temperature difference dissipation rate of turbulence is an important indicator to describe the intensity of turbulen

关 键 词：图像处理 激光散斑 神经网络 水下湍流 温差耗散率 

分 类 号：TP751.2[自动化与计算机技术—检测技术与自动化装置]