Flaw detection in aluminium die castings using simultaneous combination of multiple views

Abstract

Recently, X-rays have been adopted as the principal nondestructive testing method to identify flaws within an object that are undetectable to the naked eye. Automatic inspection using radiographic images has been made possible by incorporating image processing techniques into the process. In a previous work, we proposed a framework to detect flaws in aluminium castings using multiple views. The process consisted of flaw segmentation, matching and finally tracking the flaws along the image sequence. While the previous approach required effective segmentation and matching algorithms, this investigation focuses on a new detection approach. The proposed method combines, simultaneously, information gathered from multiple views of the scene; this does not require searching for correspondences or matching. By gathering all the projections from a 3D point, obtained from a sliding box in the 3D space, we train a classifier to learn to detect simulated flaws using all the evidence available. This paper describes our proposed method and presents its performance record in flaw detections using various classifiers. Our approach yields promising results: 94% of true positives detected with 95% sensitivity in real flaws. We conclude that simultaneously combining information from different points of view is a robust approach to flaw identification.

Applications in X-ray Testing

“…8.27e-g for multiple view analysis. 6 It is worth mentioning that in monocular detection there are false alarms, however, they can be filtered out after multiple view analysis. The reason is because false alarms cannot be tracked in the sequence or because the tracked points, when validating the corresponding points in other views of the sequence, do not belong to the class of interest.…”

A Variable Attention Nested UNet++ Network-Based NDT X-ray Image Defect Segmentation Method

“…In general, methods for automatic defect segmentation can be divided into two categories: unsupervised and supervised. A variety of unsupervised techniques are used to extract target defects from complex backgrounds [8,9], including matched filtering, morphological processing, defect tracking, etc. One advantage of unsupervised segmentation methods is that no sample annotation is required; however, the practical performance of these methods is not good, especially for small-sized defects with more blurred edges [10].…”

Blowhole Detection Based on Bidirectional Enhancement and Omnidirectional Analysis for X‐Ray Inspection of Castings

“…Kumar et al [17] presented a novel approach for multiclass weld flaw classification by means of a gray-level co-occurrence matrix-(GLCM-) based texture feature extraction technique. Previous work on a multiple-view detection method was conducted by Mery et al who introduced a method for precisely detecting defects by tracking and analyzing the correspondence between the different views [18][19][20]. Girshick et al [21] proposed a method for detection that extracted proposed regions from the input image and computed CNN features for classification.…”