To improve the accuracy of pipeline defect image detection,a pipeline digital radiography defect image detection model based on the improved RefineDet was proposed.This characteristics of limited pipeline DR defect image data and the scarcity of targets were addressed by making improvements in three aspects.Firstly,in the design of the backbone network,Swin Transformer was used instead of VGG16 as the backbone network,which enhanced the feature extraction capability while reducing the number of parameters in the backbone network.Secondly,to address the problem of limited targets in pipeline DR defect images and vulnerability to background interference,a global attention module was introduced between the backbone network and the feature fusion stage to enhance the model's focus on important features,thereby improving detection performance.Lastly,in the post-processing stage,a soft non-maximum suppression algorithm was used to remove non-optimal predicted boxes in a more reasonable way,as opposed to directly discarding non-maximum predicted boxes using traditional non-maximum suppression algorithms.The results show that the proposed method can effectively detect pipeline DR defect images.By comparing with four other commonly used object detection models,the proposed model significantly improves the accuracy of pipeline DR defect image detection.The research results can provide technical support for the detection of DR defect images.
维普
万方数据