Semantic Segmentation Experiment and Model Performance Analysis of Metal Corrosion Images Based on Deep Learning
The metal surface corrosion detection technology based on computer vision uses the deep learning model to perform pixel-level se-mantic segmentation on metal corrosion image.It extracts the texture,shape and other features of the corrosion area through model training,and judges the corrosion degree according to the corrosion area,which solves the problems of low efficiency and large error caused by manual detection.It is convenient for the subsequent automatic detection,intelligent evaluation and the establishment of pipe service failure data-base.In order to better locate and segment the corroded area,based on the public metal corrosion image dataset,the 512 ×512 pixel full im-age dataset and slice image dataset were established and the performance of deep learning models such as FCN,U-Net,PSPNet,DeepLabv3+,HRNet and SegFormer in metal corrosion detection were studied through experiments.The results indicate that all models perform better in metal corrosion image segmentation task using slice image dataset than full image dataset.Compared with other models,the SegFormer model trained on the slice image dataset has the mIoU and mPA of 66.20％and 78.55％,and the parameter amount is 3.72 MB,which gets excellent segmentation results.
万方数据
维普
