Research On Defect Evaluation Method Of Drainage Pipeline Based On Visual Computing

Drainage pipe network is one of the important infrastructures of the city,which plays a vital role in ensuring the safe operation of the city.To ensure the integrity and smoothness of the drainage network,inspectors regularly investigate the inside of the pipeline through closed-circuit television(CCTV)to determine the type,location and grade of defects.However,at present,manual interpretation is generally used to determine the defect level,which leads to low efficiency.To realize the intelligent quantitative analysis of defect level,this paper studies the current situation of drainage pipeline detection.On the basis of building a drainage pipeline defect assessment model based on visual computing,the following key algorithms are studied.(1)Aiming at the limitation of standard Hough gradient circle detection(21HT)in the inspected image of drainage pipelines,this paper proposed the restricted Hough gradient transform(RHGT)to extract the pipeline feature.In the RHGT algorithm,certain constraint strategies are given to the candidate circles to ensure that the results of the algorithm are optimized.It is veri fied by experiments that the RHGT algorithm can effectively filter the redundant candidate false circles,and the final determined pipe section circle can effectively fit the relative position of the defect.The experimental results show that deploying the RHGT algorithm in the defect evaluation model improves the average accuracy by 22.15%compared with the 21HT algorithm.(2)Aiming at the problem of insufficient defect feature extraction and poor adaptability,a defect feature extraction method based on edge detection is proposed.On the one hand,the global semantic-level defect contour is adaptively extracted based on the Holly-Nested edge detection(HED)model,and the salient edges are further extracted through image morphological operations and Canny operator.On the other hand,the YOLO-OTSU model segments detailed defect contours.The experimental results show that the proposed edge detection defect feature extraction method is self-adaptive and does not need to manually adjust the threshold value.pipelines of China,and establishes the relationship between the image features,the utility value of the defect,and the defect level.According to the polynomial regression method,different utility functions are constructed for 10 kinds of defects.The coefficient of determination R2 ranges from 0.918 to 0.99,indicating that the utility function is consistent with the results of manual evaluation.Hence,the evaluation method has been transformed from subjective language description to objective and accurate calculation.The above three key algorithms are deployed to the pipeline defect evaluation model,and experiments are carried out on the self-built Songbai dataset and LevelSewerl0 dataset.The experimental results of images show that the average absolute deviation of the proposed defect evaluation model is 2.008%,and the average accuracy rate is 86.73%compared with the manual inspection reports.The experimental analysis results of videos show that the proposed model can correctly detect the defects in the video and output the corresponding defect level,which has practical application value.