摘要
为实现锈蚀图像分割网络模型轻量化,同时消除非单一特征背景和锈液等类似特征背景干扰,本文将U_Net网络模型的编码部分替换为MobilenetV3_Large网络,导入基于ImageNet数据集的MobilenetV3_Large网络预训练权重,将U_Net网络模型解码部分的普通卷积替换为深度可分离残差卷积,并在上采样的过程中添加注意力导向AG模块和Dropout机制。经实验验证表明,本文设计的改进U_Net网络模型在非单一特征背景和锈液等类似特征背景干扰下,具有明显的锈蚀图像分割优势,相比于原U_Net网络模型,模型大小减少了81.18%,浮点计算量减少了98.34%,检测效率提升了3.27倍,即从原来不足6 fps,提升至19 fps。网络模型实现轻量化的同时,网络模型的准确率达95.54%,相比于原U_Net网络模型提升了5.04%。
In order to lighten the rust image segmentation network model and eliminate the interference of non-single feature background and similar feature backgrounds such as rust liquid,this paper replaces the encoded part of the U-Net network model with the MobilenetV3_large network,imports the pre-trained weights of the MobilenetV3_large network based on the ImageNet dataset,and replaces the ordinary convolution of the decoded part of the U-Net network model with a deep separable residual convolution.And add the attention-oriented AG module and the Dropout mechanism in the process of upsampling.Experimental results demonstrate that the improved U-Net network model designed in this paper exhibits significant advantages in rust image segmentation under non-uniform feature background and similar feature background interference such as rust liquids.The model size is reduced by 81.18%compared to the original U-Net network model,resulting in a decrease of floating point calculations by 98.34%.Additionally,the detection efficiency has improved by 3.27 times,increasing from less than 6 frames/s to 19 frames/s.While the network model is lightweight,the accuracy of the network model is 95.54%,which is 5.04%higher than the original U_Net network model.
作者
陈法法
董海飞
何向阳
陈保家
Chen Fafa;Dong Haifei;He Xiangyang;Chen Baojia(Hubei Key Laboratory of Hydropower Machinery Design&Maintenance,China Three Gorges University,Yichang 443002,China;National Dam Safety Research Center,Wuhan 430010,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2024年第2期49-57,共9页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(51975324)
国家大坝安全工程技术研究中心开放基金(CX2022B06)
湖北省教育厅科研项目(B2021036)资助。
