Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence

Abstract

This paper presents a new method for inspecting aluminum castings automatically from a sequence of radioscopic images taken at different positions of the casting. The classic image-processing methods for flaw detection of aluminum castings use a bank of filters to generate an error-free reference image. This reference image is compared with the real radioscopic image, and flaws are detected at the pixels where the difference between them is considerable. However, the configuration of each filter depends strongly on the size and shape of the structure of the casting under inspection. A new two-step technique is proposed to detect flaws automatically and that uses a single filter. First, the method identifies potential defects in each image of the sequence, and second, it matches and tracks them from image to image. The key idea of this paper is to consider as false alarms those potential defects which cannot be tracked in the sequence. The robustness and reliability of the method have been verified on both real data in which synthetic flaws have been added and real radioscopic image sequences recorded from cast aluminum wheels with known defects. Using this method, the real defects can be detected with high certainty. This approach achieves good discrimination from false alarms.

Automated Multiple View Inspection Based on Uncalibrated Image Sequences

“…Recently, a new methodology, called the Automated Multiple View Inspection (AMVI), has been developed for automated defect detection [1]. In contrast to the classic inspection methods that analyse individual images, AMVI detects defects by analysing image sequences.…”

Weld defect detection using a modified anisotropic diffusion model

“…Consequently, the detection decision can be subjective in some cases and work conditions. Several generic systems, able to carry out automatic inspection, are already marketed [1][2][3][4]. But their capacity to fault detection is limited to simple and specified applications for which the defects are well marked by only some changes in the graylevel or the form.…”

“…A two step technique to detect flaws automatically is proposed in [4] where the authors used a single filter. This method allows first to identify potential defects in each image of the sequence, and second to match and track them from image to image.…”

Detection, 3-D positioning, and sizing of small pore defects using digital radiography and tracking

“…The automatic analysis field approach is in general to segment out any possible defect [6] and then characterize it [7][8][9], for example, by its type (lack of fusion or crack etc). A merge between the segmentation part of the general automatic analysis and the 3-D point reconstruction has been shown to result in a high probability of detecting true defects and a low probability of detecting false defects, especially for low CNR defects [10]. After segmenting out the defect indications in the detector plane, there will be more false defects than true defects detected due to low CNR.…”

“…However, this need of full detection is removed in the solution proposed here. Instead of being formulated as a vision system problem and solved by epipolar geometry as in [10,11], it is explored using general tracking theory [13]. In tracking theory, the state (3-D position) of an object (defect) is tracked by assigning measurements (indications) to it as time increases (rotation).…”