MR-GA:An instance segmentation-based method for assessing underground drainage pipe defects
At present,the evaluation of defects after underground drainage pipe inspection is mainly carried out manually,which is not only time-consuming and laborious,but also low in intel-ligence,and easily affected by manual experience and visual fatigue,leading to omissions and mis-judgments and affecting the accuracy of results.In recent years,instance segmentation technology has emerged rapidly,with powerful data feature learning and description capabilities.In this pa-per,we propose a MR-GA(Mask R-CNN-Grading Assessment)method for pipeline defect assess-ment based on Mask R-CNN instance segmentation network.Firstly,we construct a model by training Mask R-CNN with self-built sample data set.Secondly,we use the constructed model to classify and segment the defects of the input pipe inspection frames.On this basis,we develop a quantitative grad-ing assessment scheme based on the characteristics of various types of pipe defects in conjunction with the Technical regulations for inspection and assessment of urban drainage pipes.Finally,we carry out pa-rameter calculation and grading assessment according to this scheme.Finally,the parameters are calculat-ed and graded according to this scheme.When the MR-GA method was applied to the actual project,the accuracy of defect category identification reached 91.34％and the accuracy of defect grading reached 88.75％compared with the manual evaluation results.
万方数据
维普
