Research On The Method Of Image Unfolding And Moving Point Splicing For Flaw Detection On The Inner Wall Of The Pipeline

Industrial thermal pipelines are widely used in social production.Due to their special high-temperature working environment,they are prone to explosions,leaks and other emergencies,which affect social safety and economic benefits.Traditional video detection technology cannot display the real pipeline inner wall information intuitively and accurately.It needs to expand and splice the pipeline flaw detection images of continuous frames of video.And for industrial pipelines with harsh environments,related processing algorithms have problems such as poor adaptability and poor real-time performance.Therefore,it is of great significance to study the image algorithm of the inner wall of the pipeline to accurately monitor the flaw detection of the inner wall of the pipeline in real time.In view of the complex background of the collected images,analyze the source of noise,use methods such as image denoising and enhancement to improve the quality of pipeline flaw detection images.Because the flaw detection images collected by industrial endoscopes are distorted and unsuitable for human observation,the Bresenham linear scanning method combined with the bilinear interpolation method is used to expand the collected flaw detection images.The specific methods of image expansion are as follows:First,use adaptive threshold segmentation algorithm and Hough circle detection to determine the center point of the image and extract the ring area.Then,use the Bresenham line scanning algorithm to expand the ring image tangentially.Finally,combine the bilinear interpolation algorithm,the image is stretched radially to improve the information richness and clarity of the expanded image.Analyzing the traditional SURF matching algorithm,which has the problems of large calculation amount and low registration accuracy.Combined with the actual situation of thermal pipes,an improved SURF-RANSAC algorithm is proposed to extract the flaw detection characteristics of the inner wall of the pipe.In the feature point neighborhood,a circular neighborhood is used instead of a rectangular neighborhood to extract 32-dimensional descriptors to achieve dimensionality reduction of feature descriptors and reduce data complexity.In order to reduce the impact of mismatch logarithm and artificial thresholds on the matching results.The adaptive threshold method is used to achieve the simple match.The feature vector is used to construct the cosine constraint optimization RANSAC algorithm to achieve the purification of matching point pairs.In order to solve the phenomenon of"dislocation" and "ghosting" in the fusion process,an improved best seam-line algorithm was used to complete the splicing experiment of flaw detection images.The simulation results show that compared with the traditional SURF algorithm,the improved matching algorithm not only guarantees the operation speed,but also has an accuracy of more than 95%.The fusion algorithm in this paper makes the stitched image transition smooth and natural,and has good robustness and applicability to the pipeline flaw detection image.