Weld defect recognition method based on improved DenseNet

There are many subjective influencing factors, poor recognition effect and low efficiency in manual evaluation of pipeline weld defects. An intelligent identification method of pipeline weld defects based on improved DenseNet network is proposed. This method firstly uses the form of multi-channel convolution of different scales to improve the DenseNet network, thereby improving the generalization ability of the network. Then, the feature extraction ability of the network is improved by stacking two convolutions of the same scale. Finally, an attention mechanism module is introduced into the dense connection block of the network to achieve the effect of improving beneficial features and suppressing useless features. The experimental results show that the method can achieve 92% accuracy in the identification of pipeline weld defects, which is about 13% higher than the original method, and has high efficiency, which can fully achieve the purpose of industrial application.