Identification and Classification of Defects in PE Gas Pipelines Based on VGG16

Abstract

For the problem of classification and identification of defects in polyethylene (PE) gas pipelines, this paper firstly performs preliminary screening of the acquired images and acquisition efficiency of defective image acquisition was improved. Images of defective PE gas pipelines were pre-processed. Then, edge detection of the defective images was performed using the improved Sobel algorithm and an adaptive threshold segmentation method was applied to segment the defects in the pipeline images. Finally, the defect images were morphologically processed to obtain binary images. The obtained binary images were applied with VGG16 to complete the training of the defect classifier. The experimental findings show that in the TensorFlow API environment, the test set’s highest accuracy reached 97%, which can achieve the identification of defect types of underground PE gas transmission pipelines.