Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels

Carrasco, Miguel; Mery, Domingo

doi:10.1007/s00138-010-0255-2

Cited by 30 publications

(8 citation statements)

References 27 publications

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“…It is necessary to examine ifM resides in the volume of the casting, the dimensions of which are usually known a priori (e.g., a wheel is assumed to be a cylinder). 2 Finally, a new matrix T 2 sized n 2 ×2 is obtained with all matched duplets (r 1 , r 2 ), one per row. If a region is found to have no matches, it is eliminated.…”

Section: Multiple View Detectionmentioning

confidence: 99%

Applications in X-ray Testing

Mery

2015

Computer Vision for X-Ray Testing

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In this chapter, relevant applications on X-ray testing are described. We cover X-ray testing in (i) castings, (ii) welds, (iii) baggage, (iv) natural products, and (v) others (like cargos and electronic circuits). For each application, the state of the art is presented. Approaches in each application are summarized showing how they use computer vision techniques. A detailed approach is shown in each application and some examples using Matlab are given in order to illustrate the performance of the methods.Cover image: 3D representation of the X-ray image of a wheel (X-ray image C0023_0001 colored with 'sinmap' colormap).

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Section: Multiple View Detectionmentioning

confidence: 99%

Applications in X-ray Testing

Mery

2015

Computer Vision for X-Ray Testing

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“…The information capture from different viewpoints can reinforce the diagnosis when a single image is insufficient [17]. In tune with the trend, four zones of LEDs with different illumination angles are used to capture the weld joints.…”

Section: Introductionmentioning

confidence: 99%

“…In this newly introduced vision system, 2D feature average gray values are extracted from the MIG welding joints and are classified using the back-propagation neural network as good weld, excess weld, insufficient weld, and no weld. In general, the calibration process is difficult to carry out in industrial environment due to vibrations and random movements that vary with time [17]. Therefore, any calibration process is not followed in this method.…”

Section: Introductionmentioning

confidence: 99%

Vision inspection system for the identification and classification of defects in MIG welding joints

Kumar

Natarajan

Ananthan

2011

Int J Adv Manuf Technol

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The variety of vision inspection systems for welding defects in the present manufacturing scenario is needed for overcoming certain limitations such as the problem of inaccuracy in the images, non-uniformed illumination, noise and deficient contrast, and confusion in defects if they occur in the same spot at the surface and subsurface. Hence, it is imperative to design a new vision inspection system which will enable to overcome the aforementioned problems in welding. A sophisticated new vision inspection system using machine vision has been developed for this study to identify and classify the surface defects of butt joint as per standard EN25817 in metal inert gas (MIG) welding. In this proposed vision system, images of welding surfaces are captured through a CCD camera. Four frames of sequence of images are obtained using four zones of LEDs using the front light illumination system in this method. From these images, the regions of interest are segmented and the average gray levels of the characteristic features of these images are calculated. The same process can be extended further to four zones (four quadrants) of four types of welded joints. Finally, welded joints can be classified into one of the four predefined ones based on the back-propagation neural network. The proposed system demonstrates an overall accuracy of 95% from the 80 real samples tested.

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“…The true defects will form paths in the detector plane as the object is rotated or translated, while the false defects will not. The performance has been improved by adding the classification step and removing the need for prior knowledge of the setup geometry in [11]. If the 3-D positions of the defects are not needed, they can instead be used implicitly as in [12] where the defects are tracked during constant translation to yield a computationally less expensive algorithm.…”

mentioning

confidence: 99%

“…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).…”

mentioning

confidence: 99%

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

Lindgren

2014

EURASIP J. Adv. Signal Process.

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This article presents an algorithm that handles the detection, positioning, and sizing of submillimeter-sized pores in welds using radiographic inspection and tracking. The possibility to detect, position, and size pores which have a low contrast-to-noise ratio increases the value of the nondestructive evaluation of welds by facilitating fatigue life predictions with lower uncertainty. In this article, a multiple hypothesis tracker with an extended Kalman filter is used to track an unknown number of pore indications in a sequence of radiographs as an object is rotated. Each pore is not required to be detected in all radiographs. In addition, in the tracking step, three-dimensional (3-D) positions of pore defects are calculated. To optimize, set up, and pre-evaluate the algorithm, the article explores a design of experimental approach in combination with synthetic radiographs of titanium laser welds containing pore defects. The pre-evaluation on synthetic radiographs at industrially reasonable contrast-to-noise ratios indicate less than 1% false detection rates at high detection rates and less than 0.1 mm of positioning errors for more than 90% of the pores. A comparison between experimental results of the presented algorithm and a computerized tomography reference measurement shows qualitatively good agreement in the 3-D positions of approximately 0.1-mm diameter pores in 5-mm-thick Ti-6242.

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Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels

Cited by 30 publications

References 27 publications

Applications in X-ray Testing

Applications in X-ray Testing

Vision inspection system for the identification and classification of defects in MIG welding joints

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

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