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.
rust area segmentationMobileNetV3U_Netattention guideddepth separable residual convolution
NETL
NSTL
万方数据
