Identification of Corrosion on the Inner Walls of Water Pipes Using a VGG Model Incorporating Attentional Mechanisms

Abstract

To accurately classify and identify the different corrosion patterns on the inner walls of water-supply pipes with different morphologies and complex and variable backgrounds, an improved VGG16 convolutional neural network classification model is proposed. Firstly, the S.E attention mechanism is added to the traditional VGG network model, which can be used to distinguish the importance of each channel of the feature map and re-weight the feature map through the globally calculated channel attention. Secondly, the joint-loss-function method is used to improve the loss function and further improve the classification performance of the model. The experimental results show that the proposed model can effectively identify different pipe-corrosion patterns with an accuracy of 95.266%, higher than the unimproved VGG and AlexNet models.