Identification of coating layer pipeline defects based on the GA-SENet-ResNet18 model

Abstract

Detecting damage in coated pipelines is a challenging and costly task. This study proposes a method for pipeline defect identification based on VMD-DWT noise reduction and GA-SENet-ResNet18. Combining wavelet transform to convert denoised defect signals into time-frequency representations enhances the model's ability to capture both time-domain and frequency-domain features of defect signals, thereby improving its recognition capability for different types of defects. The study analyzed the feature extraction capabilities of ALexNet, GooleNet, VGG16, ResNet18, SENet-ResNet18, and GA-SENet-ResNet18 models in pipeline defect recognition. Experimental results show that SENet-ResNet18 achieved an accuracy of 0.9591 on the training set in 9m38s, significantly outperforming the first four models. GA-SENet-ResNet18 achieved 96.83 % accuracy, 96.67 % precision, 96.73 % recall, and 96.68 % F1 score in pipeline defect signal recognition. Compared to ResNet18, it improved accuracy by 2.06 %, precision by 1.94 %, recall by 2.09 %, F1 score by 2.37 %, with a reduction in time by 1m1s. The study demonstrates that the combined improvement of GA and SENet enhances ResNet18 not only in feature selection and response enhancement but also significantly improves its performance compared to traditional ResNet18 networks, making it more effective in pipeline defect recognition tasks. This research is crucial for ensuring pipeline system integrity and preventing pipeline accidents.