Application of ResNet50-FPG Model in Identification of Weld Defects in Pipelines
In order to solve the problem of pipeline weld defect detection,a comprehensive scheme based on ultrasonic data processing and residual neural network(ResNet)optimization was proposed.The method of ultrasonic data acquisition and image conversion is introduced.The pruning optimization process of ResNet network model is described in detail to improve the inference speed of the model.The effect of various improved methods on the performance of the model was verified by the ablation test.The results show that the ResNet50-small-FPGM FilterPruner model has the best performance in terms of comprehensive accuracy,F1 value and inference speed,and is suitable for real-time pipeline defect detection.An effective way is provided to improve the efficiency and accuracy of pipeline weld defect detection,which has important practical application value.
万方数据
维普
