Research on segmentation method for high-consequence areas of gas pipeline based on UNet model
In order to improve the accuracy and reliability of high-consequence area image segmentation in gas pipeline facili-ty monitoring and emergency response,the UNet model was improved and optimized After the InceptionBlock module,channel attention and spatial attention mechanism methods,the model's ability to capture key features is improved,and Gaussian noise is introduced to enhance the model robustness The optimal training parameters are obtained by using the strategy of pre-serving the best parameters.Then,the segmentation effects of SE UNet,UNet++,original UNet and improved UNet models on aerial image data sets are compared and analyzed.The results show that compared with SE UNet,UNet++and the origi-nal UNet,the improved UNet model is efficient in segmentation The results show better performance,and the comprehensive performance is better than other models.At the same time,the improved UNet model improves the segmentation accuracy and reduces the risk of false detection and missing detection.The results can be flammable Provide strong support for the safe op-eration and maintenance of gas pipeline facilities.
deep learningUNet modelconvolutional neural networkhigh-consequence areaimage segmentation
万方数据
维普
