改进U_Net网络的钢结构表面锈蚀图像分割方法

Improved steel structure surface rust image segmentation method for U_Net network

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.