A detection method for oil pipeline weld defects based on X-ray images

Abstract

The invention discloses an X-ray image-based detection method for welding seam defects of oil pipelines. The present invention uses saliency conversion to process the single-channel weld grayscale image to obtain a saliency image, and the present invention proposes a fast saliency detection algorithm (Fast Visual Saliency, FVS) to obtain a saliency image. The original image and the saliency image form a two-channel input, and then the training samples are extracted through the sliding window, and LBP (Local Binary Patterns) and gray-level co-occurrence features are extracted, which are used together with the image column vector as the feature vector. The invention proposes a feature extraction method of discriminant sparse reconstruction projection (DSRP), which reduces the dimensionality of feature data and makes the algorithm more robust; finally, classifies by training an SVM classifier, thereby improving the accuracy of detection.

Description

Technical field: [0001] The invention is used in the field of pipeline welding defect detection, and particularly relates to the field of X-ray weld image defect detection. Background technique: [0002] X-ray image defect detection is a prerequisite step for defect recognition, and the result of defect detection will affect whether the recognition is correct. The defect detection of X-ray images has the following difficulties: the background of the weld has large fluctuations, the edge of the defect is blurred, the existence of welding waves makes the background of the weld more complex and changeable, and many noises interfere with the defect. Traditional segmentation algorithms cannot simultaneously overcome the above-mentioned difficulties and obtain good detection results. The results obtained by the segmentation algorithm will contain some noise, solder waves and some false defects. Usually, some detected small area targets can be removed by morphological processing,...

AI Technical Summary

Problems solved by technology

The defect detection of X-ray images has the following difficulties: the background of the weld has large fluctuations, the edge of the defect is blurred, the existence of welding waves makes the background of the weld more complex and changeable, and many noises interfere with the defect

Traditional segmentation algorithms cannot overcome the above-mentioned difficulties at the same time and obtain good detection results.

The results obtained by the segmentation algorithm will contain some noise and noise, solder waves and some false defects

Usually, some detected small area targets can be removed by morphological processing, but solder waves and false defects cannot be separated from defects

[0003] Now people have used the method of pattern recognition for defect detection, and the method of extracting features to train classifiers to classify defects and non-defects, so as to solve the problem that traditional segmentation algorithms cannot separate solder waves, pseudo defects and defects

However, the method of classifying segmented images relies too much on the segmentation results, and the usual segmentation algorithms do not have a unified evaluation standard, so there is no mature X-ray optimal segmentation algorithm

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