改进U_Net网络的钢结构表面锈蚀图像分割方法

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%。

Improved steel structure surface rust imagesegmentation method for U_Net network

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.