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In recent years, deep learning has been widely applied in diagnosing failures in oil and gas pipelines due to its powerful feature representation capabilities. However, in practical applications, the diagnostic accuracy falls short of meeting actual requirements due to complex environmental interference, coupled with existing methods that focus solely on spatial feature relationships. To improve diagnostic performance, this paper eliminates the influence of complex backgrounds through optimal neural-immune domain in the image preprocessing stage. Subsequently, we propose an Immune Depth Presentation Network that can effectively capture inter-feature correlations in the channel dimension. This network is integrated with a conventional convolutional neural network to construct the Immune Depth Presentation Convolutional Neural Network model. To validate the effectiveness and stability of the model, we compared the proposed algorithm in this paper with classic algorithms such as BPNN, KNN, VGG16, VGG19, Resnet34, and Resnet50. Our experiments show that the proposed algorithm achieved the highest average testing accuracy, demonstrating stable performance. |
