An efficient image enhancement algorithm for ImAGES OF steel reinforcing bars in concrete obtained  by a new solid state sensor based system

 

 

D.S. Benitez, S. Quek, P. Gaydecki and V. Torres

Sensing, Imaging and Signal Processing Group (SISP),

School of Electrical and Electronic Engineering.

The University of Manchester, PO Box 88, Manchester M60 1QD, United Kingdom

 

 

ABSTRACT.  This paper describes a new image processing method capable of enhancing the raw images of steel reinforcing bars in concrete obtained by a new generation of imaging instrument based on solid-state magneto-inductive (MI) sensors. The new method considers the non uniform response of the static search coil and addresses the problem of image blurring due to bar depth. Experimental results obtained for different steel bars specimens are presented showing a significant enhancement of the original image information.

 

Keywords: Inductive sensor, GMI, Metal detection, Image enhancement

 

INTRODUCTION

 

Over the past decade our research group has developed several systems capable of imaging steel reinforced bars embedded in concrete structures [1-3]. In early laboratory electromagnetic induction-based instruments, the image was formed by performing a 2D scanning over the concrete surface. In such configurations, the excitation coil travels with the sensor during the scanning process therefore producing a constant magnetic flux density at each of the scanned position; the flux distribution is uniform through the entire scanned area, which in turn results in constant curvilinear sensor response to the magnetic field perturbation over distance and a constant baseline or sensor reference for each line scan as shown in Figure 1. Recently, we have also developed a new generation of imaging instrument based on commercially available solid-state magneto-inductive (MI) sensors for imaging steel reinforcing bars in concrete [4]. In the later, however, the magnetic flux is produced by a static coil while the intensity of the magnetic flux distribution and its interaction with the metallic targets is measured by an array of fix MI sensors travelling in only one direction during the scanning. This result in a nonlinear and non-uniform sensor measurement with different baseline and sensor reference for each line scan as a consequence of the non-uniform magnetic flux distribution associated with the coil and therefore requires a new kind of image processing algorithms to both separate the different layers and to de-blur the composite image. The 2D raw images obtained by scanning system in general are blurred in nature, due to their blur function or point spread function. In this case, the blurring gets even worse due to the non-uniformity of the coil flux density and it increases with depth. Additionally, in a bar mesh, deeper bars appear fainted than those close to the surface due to the weaker signal that they generate specially when the image is normalize in relation to the strong signal returned form the upper bars [5]. Furthermore, additional noise has also been introduced to the readings by the minor misalignment of the surface mounting solid-state sensors in the sensor array PCB fabrication.

 

Several methods have been employed in the past for the case of uniform flux distribution including image subtraction [1], deconvolution [6], Hough transform [7] and polynomial-based layer separation [5] methods. The method described here, however, considers the non-uniform sensor reading response due to the static coil flux density and initial results suggest superior performance to other comparables techniques. A block diagram of the proposed new algorithm is show in Figure 2 and explained in detail in the following sections.

 

Figure 1. Typical line scan of two bars obtained by a laboratory electromagnetic induction-based instrument. The sensor reference level is the same at the begging and end of the scan line.

 

METHOD

In order to generate an image, the sensor array is scanned in only one direction by a laboratory-based motorized scanner controlled with a computer across the surface of concrete sample which contains the bar mesh. A scan step of 0.96 mm was used in the moving axis. For algorithm explanation purposes in this paper, we will consider the origin of coordinates in the scanned image located at the top left corner, and that the scan process has stared at the top of the image, the linear sensor array contains 33 single sensing elements spaced a 10 mm each [4] starting from left to right, assuming that the sensor array was oriented in the horizontal direction and that the scan was performed along the vertical direction from top to bottom in relation to the centre of the image, the result is a 2D matrix with 33 x 33 individual sensor readings at well defined space locations with each row representing a single line scan taken by the linear sensor array. To interpret the scanned signal this matrix can be mapped to a 256-bit grey scale image using a proportional linear transformation to generate a low resolution image with 33x33 pixels, which can be increased in resolution using image interpolation techniques, as described in [8]. 

 

 

 

 

Figure 2. Block Diagram of the proposed method of image enhancement.

 

Figure 3 shows a typical low resolution image obtained after scanning the sensor array across a sample containing a bar mesh which have two 12 mm diameter bars in the top and bottom layers and positioned at 100 mm depth from the plane of the sensor array as shown in Figure 4. It is clear from the image shown in Figure 2 that the raw image has a high degree of noise and blurring, and therefore it is very difficult to interpret and analyze, the bottom and top layers of the mesh are barely discernable.

 

Figure 3. Low resolution raw image.

 

 

Figure 4. Experimental Setup.

 

As shown in the block diagram of Figure 2, a smoothing algorithm, such as the method of moving averages or a low pass filtering [9] is used for pre-processing each row of the raw image in order to {smooth out} the noise introduced by the minor misalignment of the surface mounting solid-state sensors in the sensor array. Figure 5 shows a line scan taken from the middle part of the raw image before and after smoothing. Figure 6 illustrates that the outline of the bar mesh is better recognized with the filtered image. However, the discernability and visibility of the top and bottom layers remain ambiguous. From Figure 6 it is also clear that the resulting line scan after filtering has also certain degree of baseline drift, unfortunately this baseline drift varies for each row of the image and therefore it affect the quality of the image making it to appear blurring. To reduce this effect, an image de-trending stage was also introduced in the algorithm as shown in the blow diagram of Figure 2. The de-trending algorithm [9-10] removes the trend from the data of each individual row and column of the image by estimating the trend with N-polynomial curve-fitting method and then subtracting it from the original data of row or column respectively, N specifies the polynomial order to use in the N-polynomial fit. This process could be also used as an initial stage to separate the vertical and horizontal components of the image as shown in Figure 7a and 7b, the resulting rows and columns are then re-combined again into an image as shown in Figure 7c. In this case, although still a bit blur, the bottom and top layers of the mesh start to appear.

 

Further enhancement to the image can be obtained by combining the image of Figure 7c with an image obtained after processing the same image with an peak enhance algorithm based in the Hilbert transform [11]. Figure 8 show the image resulting of the peak enhance algorithm and Figure 9 shows the image obtained by combining the image of Figure 7c and Figure 8.        

 

 

Figure 5. Typical sensor array reading at the middle of the scan image, the dash line show the actual sensor readings and the solid line show the reading after misalignment correction by moving averaging filtering. The sensor response has been normalized to the maximum reading of the array. 

 

 

 

Figure 6. Raw image after filtering.

 

 

 

 (a) vertical components

(b) horizontal components

(c) image after de-trending

Figure 7. Images obtained after passing trough the de-trending algorithm.

 

 

Figure 8. Image after Hilbert transform based peak enhance algorithm.

 

 

 

Figure 9. Images obtained after combining images of Figues 7 and 6c.

 

 

A moving average filter is then used again in order to remove any noise further introduced to the image by the de-trending and peak enhancement algorithms. The Image obtained after all this process is shown in Figure 10. By comparing this image with the original raw image of Figure 3, it is clear that a significant improvement has been achieved in terms of image quality; however, some degree of blurring is still remaining in the image. This is mainly due to the non-constant and non-uniform reference level of each individual row of the processed image so far as shown in Figure 11. In Figure 11, each peak represents the presents of a metallic bar in each line scan, however, notice in this case that the bell type curve of each peak has different starting and ending points for each line scan, this differs from the typical line scan previously obtained by scanning configurations were the exciting coil travels with the sensor produces a curvilinear sensor response to the magnetic field perturbation over distance and a constant baseline and sensor reference for each line scan  as shown in Figure 1. The broadening effect of the peak increase with depth which contributes to the blurring of the image, in the case of the sensor array this effect is more severe since the sensor reference is different for each line scan as shown in Figure 11.  However, it is possible to remove this unwanted blurring of the image by removing information form each individual line scan according with a threshold level set at 0, this threshold level will  compensate the non-uniform sensor reference level of the line scan readings. This correction should be applied to both rows and columns of the image. Figure 12 shows the final de-blur image after sensors refence level correction and interpolated to a 320x320 pixels resolution. In this image the top and bottom bars of the bar mesh are now clearly discernible and the improvement in relation to the raw image of Figures 3 and 6 is quit remarkable.      

 

 

Figure 10.  Image after further filtering of image in Figure 9. 

 

Figure 11. Individual rows of image of Figure 9, plotted as individual line array readings. Each line scan has different baseline and different staring and ending points.

 

 

 

Figure 12.  Final Image after further processing of image in Figure 11. 

 

 

 

RESULTS AND DISCUSSION

Several scans of bar mesh configuration with bars of different diameters at different distances were conducted with a sensor array system and the obtained raw images were processed using the new image processing algorithm in order to determine its performance. Figure 13 shows the images obtained before and after processing. The images shown are high-resolution 320 ´ 320 pixel images generated by applying image interpolation techniques to the original low resolution 33x33 pixel images generated by the scanner.  

It can be seen from the results obtained in Figure 13 that a remarkable improvement in the image quality has been obtained in each case, by using the processed with the proposed algorithm the images information content of each image has been enhanced and therefore the original structures are now clearly visible and discernible. Although complex, the algorithm is relatively easy to implement and it is an essential precursor in the development of a real time scanner which will generate real video of the rebar structures. With minor modifications it can also be applied to de-blur images obtained by previous laboratory electromagnetic induction-based instruments.

 

 

  Sample mesh

Raw  Image

Final Image

12 mm bars at 100 mm depth

12 mm bars at 100 mm depth

12 mm bars at 100 mm depth

12 mm bars at 110 mm depth

10 mm bars at 100 mm depth

12 mm bars at 70 mm depth

 

Figure 13. Image obtained by scanning different bar mesh configurations at different depths. The raw and processed images are shown in 320X320 pixels resolution.

 

 

CONCLUSION

 

A new method has been developed for image enhancement of steel reinforcing in concrete obtained by a sensor array based sensor. Experimental results show that the method is not only robust and repeatable but also computationally efficient; it can also accommodate different depths and configurations. The new method considers the non-uniform sensor array readings response due to the static coil flux density and the results obtained suggest superior performance to previous techniques.

 

ACKNOWLEDGEMENT

 

The authors wish to express their gratitude to the Engineering and Physical Sciences Research Council in the UK for financially supporting this work.

 

REFERENCES

 

1.       P. Gaydecki, and F. Burdekin, “An inductive scanning system for two dimensional imaging of reinforcing components in concrete structures”, Measurements Science and Technology, vol. 5, 1994, pp. 1272-1280.   

2.       G. Miller, P. Gaydecki, S. Quek, B. Fernandes and M. Zaid, “A combined Q and heterodyne sensor incorporating real-time DSP for reinforcement imaging, corrosion detection and material characterization”, Sensors and Actuators A: Physical, vol. 121, Issue 2, June 30, 2005, pp. 339-346.

3.       P. Gaydecki, S. Quek, G. Miller, B. Fernandes and M. Zaid, “Design and evaluation of an inductive Q-detection  sensor incorporating DSP for imaging of steel reinforcing bars in concrete”, Measurements Science and Technology, vol. 13, 2002, pp. 1327-35.

4.       D. Benitez, S. Quek, P. Gaydecki, and V. Torres, “ A 1D Solid state sensor based array system for real time magnetic field imaging of steel reinforcing bars embedded within reinforced concrete”, IEEE Trans. Instrum. Meas. (submitted).

5.       S. Quek, P. Gaydecki, B. Fernandes, G. Miller, “Multiple layer separation and visualization of inductively scanned images of reinforcing bars in concrete using polynomial-based separation algorithm”, NDT&E international, 35, 233–240 (2002).

6.       P. Gaydecki, K. Glossop and F. Burdekin, “A prototype inductive scanning system for two-dimensional imaging of metal reinforcing components in concrete system design and data visualization”, The International Symposium of NDT in Civil Engineering (NDT-CE) in Berlin, Germany, September Vol.1, 1995, pp. 745-752.   

7.       B. Fernandes, I. Silva, P. Gaydecki, “Vector extraction from digital images of steel bars produced by an inductive scanning system using a differential gradient method combined with a modified hough transform”, NDT&E international, 33, 69–75 (2000).

8.       M. Zaid, P. Gaydecki, S. Quek, G. Miller and B. Fernandes, “Image reconstruction of steel reinforcing bars in concrete using fourier-domain interpolation applied to a sparsely populated data set”, Journal of Nondestructive Evaluation, vol. 18, No.3-4, September-December, 2002, pp. 119-130.

9.       C. Chatfield, “The analysis of time series”, 6th Edition, CRC press, 2004.

10.   R. Shumway, D. Stoffer, “Time series analysis and its applications”, Springer-Verlag, 2000.

11.   D. Benitez D, P. Gaydecki, A. Zaidi, A. Fitzpatrick, “The Use of the Hilbert Transform in ECG Signal Analysis”, Computers in Biology and Medicine, 31, pp. 399-406.