基于VGG网络的轻量化小型水下构筑物探伤模型  

Flaw Detection Model of Lightweight Small Underwater Structures Based on VGG Network

作　　者：冯家乐 陆伟 陈玮 杨雪[1] Feng Jiale;Lu Wei;Chen Wei;Yang Xue(School of Innovation and Entrepreneurship,Nanjing Institute of Technology,Nanjing 211100,China)

机构地区：[1]南京工程学院创新创业学院,南京211100

出　　处：《兵工自动化》2023年第8期61-64,96,共5页Ordnance Industry Automation

基　　金：江苏省大学生创新训练计划(202111276010Z,202011276007Z)。

摘　　要：针对水下构筑物伤痕形态随机多变,特征提取困难,导致水下探伤识别精度较低的问题,提出一种基于视觉几何组(visual geometry group,VGG)网络的轻量化小型VGG(lite small visual geometry group,LSVGG)模型。采用经典VGG网络结构,减少卷积层和提高特征数量的方法在保证识别精度的前提下降低运算时间和系统开销。实验结果表明:该LSVGG模型可以部署在小型无缆水下机器人(autonomous underwater vehicle,AUV)上,具有较高水下构筑物探伤识别精度;与传统模型相比,水下构筑物探伤识别精度提高了近一倍,识别准确率高达99.7%。Aiming at the problem of low recognition accuracy of underwater flaw detection due to the random and changeable shape of underwater structures and the difficulty of feature extraction,a lightweight small visual geometry group(LSVGG)model based on visual geometry group(VGG)network is proposed.The classical VGG network structure is adopted to reduce the convolution layer and improve the number of features,which can reduce the operation time and system overhead on the premise of ensuring the recognition accuracy.The experimental results show that the LSVGG model can be deployed on the small autonomous underwater vehicle(AUV)and has high flaw detection and identification accuracy for underwater structures.Compared with the traditional model,the recognition accuracy of underwater structure flaw detection is nearly doubled,and the recognition accuracy is as high as 99.7%.

关 键 词：VGG网络 轻量化 水下构筑物探伤 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]