Research On Image Recognition Of Oil Pipeline Leakage Based On 2D-VMD Algorithm

With the continual expansion of oil and gas pipeline network in domestic,and more and more oil and gas pipelines have been laid,which lead to frequent oil and gas pipeline leakage incidents.Hence,it has drawn the great attention for people to realize the issue of oil pipeline leakage.In the article,the combinations of digital image processing technique and detection technique are applied to the leak detection of oil pipelines under the circumstances of bad environment and artificial failure to realize emergency observation in time.Firstly,in view of the problem that the pipeline image signals are often disturbed by noise in the process of acquisition and transmission,an image denoising model based on the combination of two-dimensional variational mode decomposition algorithm(2D-VMD)and Hausdorff distance is proposed.The 2D-VMD is used to decompose the noisy image into several intrinsic mode functions(IMFs),then,the Hausdorff distance is employed to acquire the distance between the probability density function distribution of the decomposed IMFs and the probability density function distribution of the original image.The obtained noisedominated intrinsic mode functions after wavelet denoising are reconstructed with the signaldominated intrinsic mode functions to realize image de-noising.In the experiment,to verify that the proposed model can effectively remove noise and get clearer images,the denoising effects of various algorithms are compared.Secondly,the Gabor filter has a good advantage in the representation and differentiation of image texture.The locally linear embedding algorithm(LLE)has both the advantages of linear method and the characteristics of nonlinear algorithm in the feature dimension reduction.Therefore,combining 2D-VMD,Gabor filter and LLE algorithm of an image feature extraction and dimension-reduction algorithm is proposed.In the algorithm,the images are decomposed by 2D-VMD,and the decomposed IMFs are extracted feature by Gabor filter.Then,the feature dimensions are reduced by LLE to obtain sensitive feature parameters that are beneficial to classification.The simulation results show that the designed feature extraction and dimensionality reduction model can effectively extract sensitive features information of pipeline images.Finally,to resolve the difficulty of limited sample classification,the support vector machines algorithm is employed to identify two working conditions of pipeline leakage images and pipeline non-leakage images.To improve the classification accuracy of oil pipeline images,the particle swarm optimization algorithm is employed to optimize the two parameters of c and g in SVM.The simulation results show that the algorithm proposed can effectively identify the leakage of oil pipeline images,meanwhile,it possesses a good classification effect.