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%.
作者
冯家乐
陆伟
陈玮
杨雪
Feng Jiale;Lu Wei;Chen Wei;Yang Xue(School of Innovation and Entrepreneurship,Nanjing Institute of Technology,Nanjing 211100,China)
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)。
关键词
VGG网络
轻量化
水下构筑物探伤
VGG network
lightweight
crack detection of underwater structure
作者简介
冯家乐(2001—),男,安徽人。
