FUSION OF MULTIMODAL NDE DATA FOR IMPROVED CORROSION DETECTION

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1 ICONIC 2007 St. Louis, MO, USA June 27-29, 2007 FUSION OF MULTIMODAL NDE DATA FOR IMPROVED CORROSION DETECTION K. Gupta 1, R.J. Stanley 1, M.T. Ghasr 1, S. Kharkovsky 1, R. Zoughi 1, G. Steffes 2 1 Electrical and Computer Engineering Department University of Missouri-Rolla, Rolla, MO Phone: , Fax: stanleyj@umr.edu 2 Air Force Research Laboratory (AFRL) Wright Patterson, OH Phone: Gary.Steffes@wpafb.af.mil Abstract. Corrosion is a significant maintenance problem, especially for aging aircrafts, with the Air Force recently estimating corrosion maintenance costs exceeding $800M per year. Nondestructive evaluation (NDE) techniques can be used to detect corrosion under paint and hidden within layered structures. However, it is difficult to capture a comprehensive picture of the corrosion environment using any single NDE technique. This research presents a data fusion approach for combining raw multimodal NDE data for detecting surface and subsurface corrosion, which facilitates limiting false corrosion detections. Experimental results are presented using NDE techniques, including microwave, eddy current, ultrasound, and radiography for a lap-joint mimic. Keywords: Corrosion, Data Fusion, Eddy Current, Microwaves, Nondestructive Evaluation. 1 INTRODUCTION NDE methods have been used to detect corrosion under coatings such as paint, etc. [1], as well as subsurface corrosion in layered structures [2]. Near-field microwave NDE techniques, utilizing open-ended rectangular waveguide probes, have been successfully used in detecting and evaluating corrosion precursor pitting under paint due to their high spatial resolution [3]. However, microwaves do not penetrate highly conducting materials such as metals. Therefore, in a lap joint made of several aluminum panels, other methods must be employed to detect corrosion in between the various layers of the structure. Eddy current (EC) NDE techniques can detect corrosion through metals and are also sensitive to corrosion severity [2]. Eddy current inspection systems are small, portable, inexpensive and capable of providing effective metal loss information. Radiographic NDE techniques can also be used to detect the presence of corrosion in layered structures [2]. Consequently, combining data obtained from multiple NDE modalities, i.e. data fusion, has the potential to give a comprehensive snapshot of the corrosion environment in layered structures. A preliminary study has indicated the usefulness of fusing microwave NDE and eddy current inspection data [4]. In this study, data fusion techniques are investigated for fusing raw 407

2 multimodal data and performing corrosion detection from structures using NDE methods, including microwave, eddy current inspection and radiographic inspection. Fusion of raw NDE data is performed using three different techniques, including the Hotelling transform [5], a dissimilarity measure-based fusion algorithm [6], and a wavelet transform-based technique [7]. Statistical and data driven fuzzy logic-based anomaly detection methods are examined for automatic detection of corrosion areas from the fused NDE data [8,9]. Fusion of the anomaly detection results is performed using an extension of the maximum likelihood approach to data to limit false corrosion detection [10]. 2 DATA FUSION ALGORITHM The data fusion process investigated is shown in Figure 1. First, fusion is performed at the raw data level. NDE images from a single modality are fused to provide enhanced contrast and reduced noise levels in the resultant images. Fusion of NDE images from different modalities improves image quality and also provides complementary information in the composite image. Three techniques were investigated for raw NDE data fusion and are discussed in following sections. Next, RX and fuzzy logic-based anomaly detectors are presented for automated detection of corrosion areas from the fused images. Finally, a maximum likelihood approach is described for fusing the corrosion detection results from the RX and fuzzy logic-based methods to reduce false corrosion detections. Figure 1: Data fusion process overview. 2.1 Hotelling Transform-Based Data Fusion Algorithm The first raw data fusion method examined was the Hotelling transform [6]. This method uses multiple spatially registered NDE images, each with M rows and N columns, which are expressed as a unit by grouping together the gray values at corresponding pixel position to form vectors. If there are n such images then there will be a total of K= MN vectors, each of size n x 1. T Let x ( i, j) = [ x 1(i,j),x 2(i,j),...,x n(i, j) ] denote the column vector of gray values from the registered modal image data x1, x 2,., x n, and let their mean and covariance matrix be denoted as C x, respectively. Let A denote the eigenvector corresponding to the largest eigenvalue of. The resulting pixel value at each (i,j) for the fused image, y, is given in equation (1). m x and C x y(i,j) = A( x(i, j) mx ) (1) 408

3 2.2 Dissimilarity Measure-Based Data Fusion Algorithm The second method investigated was based on a dissimilarity measure to highlight the degree of disagreement between an image pixel and its neighbors [5]. This dissimilarity measure-based algorithm was used in this research to generate a single fused image from multiple spatially registered NDE images. The algorithm retains the salient features of the individual images (modalities) being fused. The algorithm uses a local window of an appropriately chosen size around each pixel in the NDE image. Equation (2) gives the pixel value at each location of resulting fused image, denoted as d. From equation (2),. represents the Euclidian norm. E represents the four connected neighborhood of the current pixel location (i,j) and a set of randomly selected pixels, at coordinates (i,j ), within a local window centered at (i, j). x(i, j) and x(i, j ) are the band vectors at pixel locations (i,j) and (i,j ) respectively and N(i,j) denotes the number of pixels in the set E. d(i,j) = x(i, j) x(i, j) (i,j) E 2.3 Discrete Wavelet Transform-Based Data Fusion Algorithm N (i,j) The third method explored was based on the Discrete Wavelet Transform (DWT) [7]. In the sub images obtained in the transform domain, a larger absolute value corresponds to a sharper change in brightness and, therefore, to regions such as lines, edges, region boundaries in the images. The maximum absolute value at each pixel location (i,j) was taken to generate the fused image in the transform domain. The pixel based fusion rule is summarized in equation (3). { } T(F) (i, j) = max T(x ) (i, j),t(x ) (i, j),...,t(x ) L (i, j) (3) L 1 L 2 L n T( F) L ( i, j) denotes the DWT coefficients of the fused image F at position (, j) i of scale factor L, x 1,x 2,,x n denote the images to be fused. Once the sub-bands are fused in the transform domain the inverse discrete wavelet transform of the fused coefficients is obtained to yield the fused image in the spatial domain. For the multimodal data examined in this research, F was determined using db10 at scale factor L= Anomaly Detectors Two anomaly detectors were applied to the fused raw data images. First, the RX detector [8] provides a rotation invariant approach to anomaly detection. The RX detector compares the covariance within a circular target region to the covariance of an outer annular region in generating a statistically-based anomaly confidence value used for corrosion detection. Second, a fuzzy logic-based anomaly detector was examined [9]. If B denotes a fuzzy set representative of the gray levels associated with the non-corroded areas or the background in the fused images, the fuzzy clustering ratio is ratio of the number of 8-connected neighbors for a pixel (i,j) that have a specified membership in B to the number of pixels in the 8-connected neighborhood that (2) 409

4 have non-zero membership in B. An extension of a maximum likelihood approach [10] to data fusion was used to fuse results obtained from the two anomaly detectors. 3 EXPERIMENTS PERFORMED Three aluminum plates, each measuring 12 x 12 x 0.04 were corroded in a salt fog chamber with varying exposure times from one day to five days resulting in varying corrosion levels. A lap joint like structure was mimicked by stacking the panels as shown in Figure 2. The top panel was painted subsequent to having been corroded. Microwave NDE images of the lap joint mimic were obtained using near-field microwave reflectometers with open-ended rectangular waveguide probes at V-band (50-75 GHz) and W-band ( GHz). Also, eddy current and radiography image data for a lap joint specimen obtained from a retired aircraft was provided by the Air Force Research Lab (AFRL). Paint Layer 4 RESULTS AND DISCUSSIONS Figure 2: Schematic of stacked panels mimicking a lap joint. The results for the data fusion experiments performed are summarized in Figures 3-5. Figure 3 (a) shows the radiographic image of a lap joint specimen from a retired aircraft in which circular corrosion patches were simulated. Figure 3 (b) gives the eddy current image of the same panel. The two images were fused using the dissimilarity index-based fusion algorithm, with the raw data fused image shown in Figure 3 (c). The fused image highlights the circular corrosion patches while suppressing the indications of the rivet heads. The RX and fuzzy anomaly detector results for the fused image are shown in Figure 3 (d) and (e), respectively. Figure 3 (f) gives the detector level fusion image obtained using the maximum likelihood technique for combining the RX and fuzzy detector results. (a) (b) (c) (d) (e) (f) Figure 3: Fusion results using dissimilarity index based algorithm, (a) radiograph image, (b) eddy current image, (c) data level fusion image, (d) RX anomaly detector, (e) fuzzy anomaly detector, (f) detector level fusion image 410

5 Figure 4 (a) shows a W-band microwave image of the lap joint mimic specimen shown in Figure 2. The V-band microwave image of the same specimen is shown in Figure 4 (b). The two images provide indication of the presence of corrosion under paint in the top layer of the specimen. However, individually the images lack contrast and also do not exhibit sharp transitions between the corroded and pristine regions. The image obtained by fusing the V-band and W-band microwave images is shown in Figure 4 (c). The fused image exhibits a greater overall contrast as well as sharper edges highlighting the transitions between the corrosion patches and the background. The RX and fuzzy anomaly detector results for the fused image are shown in Figure 4 (d) and (e), respectively. The detector level fusion image obtained by fusing the results of the RX and fuzzy detector is shown in Figure 4 (f). (a) (b) (c) (d) (e) (f) Figure 4: Fusion results using Hotelling transform based algorithm, (a) W-band microwave image, (b) V-band microwave image, (c) data level fusion image, (d) RX anomaly detector, (e) fuzzy anomaly detector, (f) detector level fusion image Figure 5 (a) shows the V-band microwave image of the lap joint mimic specimen. Figure 5 (b) gives the eddy current image of the specimen at a frequency of 2 khz. The microwave image provides indication of corrosion under paint in the top layer while the eddy current image reveals corrosion occurring in deeper layers. Figure 5 (c) presents the fused image obtained using the discrete wavelet transform-based algorithm. The corrosion schematic of the lap joint mimic specimen shown in Figure 5 (d), reveals that the fused image in Figure 5 (c) provides corrosion information under paint as well as in deeper layers of the lap joint specimen. 5 SUMMARY The current investigation demonstrates that fusion of raw multimodal NDE data is able to provide a comprehensive snapshot of the corrosion environment. Anomaly detection on the fused images and subsequent decision level data fusion helps in limiting false corrosion detection. Preliminary studies have shown that data fusion techniques can be used to assess the residual structural integrity of lap joint mimics [4]. The use of multimodal fusion, at both the raw data level and the decision level, to evaluate the structural integrity of real life aircraft structures such as lap joints may be examined in future studies. 411

6 (a) (b) (c) (d) Figure 5: Fusion results using the discrete wavelet transform based algorithm, (a) V-band microwave image, (b) 2 khz EC image, (c) data level fusion image, (d) Corrosion schematic of lap joint mimic panel. REFERENCES 1. R. Zoughi, Microwave Non-Destructive Testing and Evaluation, Kluwer Academic Publishers, The Netherlands, Nondestructive Evaluation: Theory Techniques and Applications, Edited by P.J. Shull, publisher, Marcel Dekker Incorporated, 2002, pp , M. Ghasr, S. Kharkovsky, R. Zoughi, R. Austin, Size Evaluation of Corrosion Precursor Pitting Using Near-Field Millimeter NDT Methods, in Review of Progress in Quantitative Nondestructive Evaluation 24, edited by D.O. Thompson and D.E. Chimenti, AIP Conference Proceedings, vol.760, American Institute of Physics, Melville, NY, 2005, pp K. Gupta, M.T. Ghasr, S. Kharkovsky, R. Zoughi, R.J. Stanley, A. Padwal, M. O Keefe, D. Palmer, J. Blackshire, G. Steffes, N. Wood, Fusion of Microwave and Eddy Current Data for a Multi-Modal Approach in Evaluating Corrosion Under Paint and in Lap Joints, in Review of Progress in Quantitative Nondestructive Evaluation, 2006 (in press). 5. S.K. Pal and A. Pal, Pattern Recognition: From Classical to Modern Approach, World Scientific Publishing Co. Pvt. Ltd., 2001, pp R.C. Gonzalez R.E. Woods, Digital Image Processing, 2nd ed, Upper Saddle River, New Jersey: Prentice Hall, X.E. Gros, J. Bousigué, K. Takahashi, Experimenting with Pixel Level NDT Data Fusion Techniques. IEEE Transactions on Instrumentation and Measurement, vol. 49, No. 5, October S. Agarwal, P. Sriram, P.P. Palit, O.R. Mitchell, Algorithms for IR imagery based airborne landmine and minefield detection, Detection and Remediation Technologies for Mines and Mine like Targets VI, Proceedings of SPIE, vol. 4394, 2001, pp R.J. Stanley, R.H. Moss, W.V. Stoecker, C. Agarwal, A Fuzzy Based Histogram Analysis Technique for Skin Lesion Discrimination in Dermatology Clinical Images. Computerized Medical Imaging and Graphics, vol. 27, no. 5, 2003, pp Hackett, M. Shah, Multisensor Fusion: A Perspective, Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, 1990, pp