Application of IR thermography for evaluating the integrity of a natural gas delivery station

Aleksander Nawrat, Janusz Skorek, Karol Jędrasiak, Krzysztof Daniec, Wojciech Kostowski, Roman

Koteras, Adam Czornik, Barbara Mendecka, Silesian University of Technology:

Gliwice, Poland

Dariusz Jarczyk Górnośląska Spółka Gazownictwa Sp. z o. o.

Zabrze, Poland

Damian Kasprzak WASKO S.A.

Gliwice, Poland

Abstract—The paper explores possibilities of applying the IR thermography for integrity evaluation and quick detection of failures in natural gas delivery stations. The natural gas stations typically comprise overground facilities for filtering, pressure reduction, flow metering and odorizing. The facilities and the interconnecting pipelines are subject to risk of damages due to material failures or external factors. The failure detection algorithm presented in the paper is based on two physical phenomena concerning the temperature of piping. The first one is bound to the process of natural gas expansion to the atmosphere leading to a rapid decrease of the gas temperature, usually below the ambient temperature. Therefore, the flux of gas absorbs heat from the pipe walls implying a local drop of the surface temperature. The second phenomenon is related to the change of the flow pattern in the station, since leakages typically induce high discharge flow rates. The new flow pattern affects the thermal effect exerted by the gas flux on the pipe walls, hence significant variations from previous values as well as from the ambient conditions may be registered in short-time intervals. The algorithm implements integrity test procedures based on both thermal mechanisms, as well as initializing procedures for image normalization and identification of relevant pipeline sections.

Keywords: Image processing, gas leaks detection, low-cost thermal imaging

I. INTRODUCTION Tubular systems are widely exploited because of their

possibilities to transport different mediums, starting from liquids and ending with fumes. They can be used to transfer harmless substances as well as those that may be considered as a potential danger for both: environment and human's life. The pipes detection problem, and afterwards the issue of analyzing cracks and deformations are acknowledged as very important and crucial for preserving a good quality of installations and preventing eventual damage entailed with transfer mediums leaking.

The problem is clearly visible in case of gas pumping stations. During pipe changes often cracks and holes could appear causing small gas leaks. It is easy to detect a massive gas leak however there are often multiple small leaks causing great cost but very difficult to find. Currently used methods

often require a specially trained person presence. Because of the gas leak the area is extremely dangerous therefore in order to reduce the danger and minimize the cost of leaks it is essential to design methods of contactless gas leaks detection.

II. LITERATURE STUDY Pulsed induction methods allow to generate a conductive

current at the surface and then detect eddy currents which are induced in metal objects. This process can be used in case of detecting metal pipes. Unfortunately, it has doubtful meaning within the area of finding plastic or large pipes. Moreover, there is also a problem with low speed of collecting data. In the event of detecting pipes using magnetic locators the induced magnetic field is measured to detect all objects with ferromagnetic features. Unsuccessfully, this method is not only expensive, but also has important constraints within the area of materials with poor magnetic properties. Electromagnetic locators, which work similarly to magnetic ones, have congruent constraints which are the reason of many problems with detecting non-metal pipes. There was an attempt made to establish ways of pipes detection, using the information about the resistivity of the material. However those methods not only restrict the spectrum of detected materials to metal, but also have the lowest speed of obtaining data. Acoustic methods allow to detect the pipes made from every material, but also generate a big amount of false readings. What is more, because of the attempt to reduce the number of false alarms, the obtaining data speed of those methods is very slow [1].

In order to design a method that is independent of the pipelines material another data acquisition medium could be used. It was decided to use low cost thermal imaging cameras. During gas leak due to significant speed of the gas extension the temperature of the pipe around the crack is rapidly cooling. The change in temperature should be visible in a long term observation. This way of realization of the given task is possible mainly because the designed system is going to be used in a limited spaces. What is more, application of this kind of method gives an opportunity to create a system which, in contradistinction to previous ones, can be used to monitor the tubular system automatically, without the constant presence of human operator. This kind of solution makes it possible to

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reduce the costs, get a higher quality of work and also increase the safety [2]. What is more, data acquired that way can be used directly to analyze the technical condition of observed pipelines.

Because of the fact that depending on the work environment the analyzed pipes are going to be found in more or less contrasting colors, there is no possibility to use color-based algorithms during the detection of the area of interests. In this connection the crucial factor allowing the analysis is the edge appearance. Basing on this conclusion, the algorithm used within this task should get good results when searching for objects with well-defined borders. A good example of this kind of algorithm is the CHEVP method. Even though this algorithm definitely produce good results, there are still less complicated algorithms, such as Hough's transform [3] or Canny's algorithm [4] searched. In case of using those methods during the analysis of above-mentioned issues satisfactory results can be achieved within the detection of potential areas of interests, what in considerable scale follows the extent of the algorithm's advancement [5]. Because of the fact that mentioned algorithms are composed from many iterative steps, there are consequences such as its low computable efficiency. Due to the fact that most of the gas leaks detection and pipelines condition analysis is based on the offline processing [6] of the video material, acquired earlier with using of cameras installed on remote-controlled robots, not infrequently this observation is not taken into consideration.

In case of the elaborated one it is assumed that the tubular system analysis is going to be proceeded in real time. Because of this observation it is considered to use a combination of more simple algorithms that permit achieving similar final results [7]. Using this kind of solution allows the algorithm to get almost the same results in online processing as in the offline one. However the greatest potential advantage of such system might be an ability to detect potential leaks before they happened.

Much lower computational complexity of using edge detection methods [8] in comparison to the other above-mentioned methods allows to implement the contactless gas leaks detection as an additional functionality to the existing surveillance systems. Proposed way of processing the image data allows to monitor the condition of pipelines and looking for the potential danger for human beings in buildings with high level of peril in the same time. Because of the possibility of human trespassers detection there is also instant alarming possible in case of situations, like finding undesirable people in the observed area.

III. ACQUISITION The video source used during test recording was a thermal

imaging camera FLIR SC 305 (fig. 1). The camera is capable of streaming thermal images that consists from 76800 individual picture elements sampled with a 16 bit resolution. The camera is using an uncooled microbolometer detector and optics that allows observing 25° x 18.8°. Detector pitch is 25 µm and its spectral range is from 7.5 to 13 µm. Perceived temperatures can vary from -20°C to +120°C with accuracy of ±2% of reading. Thermal sensitivity of the camera is <0.05ºC

at +30ºC (+86ºF) / 50 mK. The device minimum focus distance is 0.4m and standard focal length is 18mm (F 1.3).

Fig. 1. Flir SC 305 thermal imaging camera.

The output stream is 320x240 16-bit pixels resolution and

possible to acquire using IEEE 802.3 Ethernet networks. Thanks to network type image streaming it is easy to simultaneously observe, record and analyze the data. It was one of the specified requirements for the test recordings. Because of the compatibility with GigE Vision and GeniCam standards it was easy to acquire the video stream from the camera.

The described camera was not mounted in a secure laboratory but instead was extensively used outdoors during the recordings it was necessary to pay attention that the device is rugged and with possibility to easy mounting outdoor. The used camera housing meets the IP 40 standard and can withstand the vibrations up to 2g. The operating temperature range is from -15°C to +50°C. The camera size is 170x70x70 mm and weight is 0.7kg.

In order to implement the algorithm on real time stream from the camera a Flir SDK was required. It is an library of essential programming interfaces to the Flir cameras accompanied by sample mini programs for viewing and recording the stream from selected cameras. Using the SDK it was possible to implement an prototype software application that was used in order to contactless detect gas leaks. Using SDK was required in order to turn off automatic focus and image correction algorithms implemented in the camera. Automatic contrast is a useful feature for a human observer however it drastically changes the image values distribution depending on the observed scene. In order to acquire comparable results it is mandatory to use manual predefined settings.

IV. ALGORITHM The main requirement for the proposed algorithm (fig. 4)

for the contactless gas leaks detection using thermal imaging cameras is short computation time in order to perform processing synchronously with the incoming stream data. The algorithm is divided into three main parts: preprocessing, ROI detection and leaks detection. The processing loop starts with an image acquisition. In this stage using Flir SDK an image frame is grabbed from the GeniCam compatible network. In order to detect the pipe region in the image a preprocessing step is required. Because the pipes are often in various colors the reasonable method of detection is by using its shape edges.

Preprocessing is a step of detection edges in the image. However the fast edge detectors are very sensitive to noise in

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the images especially from the IR cameras a Gaussian filtering is performed. The Gaussian blur is a one of the low-pass filters, a 2-D convolution operator. It is used to reduce the negative effects of noise and preserve the edges. The values of the pixels within the considered area are averaged in a weighted manner. The Gaussian blur impulse response is a Gaussian function (1):

2

2

4)2/12/(

21 σ

πσ

+−−

=nk

k eg,

(1)

where σ is the parameter changing the slope of the function, n is the length of smoothing window and nk <<0 . The smoothed signal is calculated by the formula (2):

∑+

=−=

nN

jjkjk gxy

0 , (2)

where ky is the filtered part of the image. By using the weighted average of each pixel the values of the other pixels are close to the central pixel. Therefore Gaussian blur provides gentler smoothing and preserves edges better than the mean filters of similar size.

As a fast edge detector a Scharr operator was used [9]. The Scharr operator uses usually two 3x3 kernels which can be transposed and rotated. It allows to the determination of gradients in different directions. The kernels are convolved with the original image. To eliminate the effect of mutual compensation of positive and negative patterns of masks there are used formulas such as modular formula (3):

,21 LLL += (3)

where L is the result image and 1L , 2L are respectively vertical and horizontal gradients estimated by convolving the source image with the Scharr kernels (4).

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−

=⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−=

30310010303

31030003103

21 LL (4)

Pipe ROI detection is based on assumption that in image exists a strong, straight edge possible to detect by the edge detector during preprocessing step. For straight lines detection Hough line transform [10] was used. It transforms between the Cartesian space and a parameter space in which a straight line can be defined. Lines can be defined using equation (5):

,sincos ryx =+ ϕϕ (5)

where ϕ is the angle and r is the line radius (fig. 2).

Fig. 2. The representation of line using angle and radius.

In the parameter space of r and ϕ the line is represented

as a sine curve and sinusoids corresponding to co-linear points interest at an unique point in the parameter space (fig. 3). Simple peak searching algorithm is enough to find a parameters combination that can be used to express the line in the Cartesian space.

Fig. 3. An example Hough line parameter accumulation space. The darker the

color the more accumulated value.

Acquired from Hough transform parameter accumulation space if filtered for the most significant peak. It is assumed that there is only one interesting pipe in the image therefore only one peak is searched in the parameter accumulation space. Finding the maximum peak can be implemented as a simple finding a maximum value in a two dimensional accumulator space. In case of detecting analytical shapes described with more than two parameters it is required to look for maximum value in higher dimensional space. After finding the peak coordinates and at the same time the line parameters the corresponding line and a pipe can be marked at the source image.

However in order to estimate the rectangular ROI a height of the pipe is required. The parameter is unknown from the Hough line detection therefore it is required somehow estimate it. At the moment the problem was not researched and simple choosing one from the most popular pipe height values was used.

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Fig.4. The block schema of the proposed algorithm.

When pipe ROI is estimated actual leak detection can be

performed. In order to detect leak a normal pipe temperature has to be computed. It could be computed as a simple mean or median over the ROI and some number of the algorithm

iterations. In order to allow the algorithm for operation during the day and night it was decided to use adaptive background model computed using the formula (6):

( ) ttt BIB αα −+=+ 11 ,

(6)

where:

B – background model,

I – current frame temperature value, α - speed of algorithm’s adaptation to changes.

The parameter of speed of adaptation is assumed to be very low in order to adapt to the changes in terms of hours rather than milliseconds. It was chosen as 0.00003. A detected gas leak can be defined as a region in image characterized by the temperature values significantly different than the background model of the pipe. The assumption can be tested by thresholding using the equation (7):

τ>− tt BI, (7)

where τ is an experimentally set threshold value for the detection. If there exist pixels with values above the threshold they are considered as leaks or other pipe anomalies worth further inspection.

V. EXPERIMENTAL RESULTS The proposed algorithm was tested using real time video

streams from the outdoor thermal imaging camera observing gas leaks tests. It was a test of capabilities of low-cost vision systems for contactless gas leaks detection. The observed scene (fig. 5) consists of the main pipe visible in the center of the screen. However the scene is complex and in the background a security fence and a meadow is visible. The gas leaks experiments were conducted in a controlled environment. The size of the hole in the pipe was known and the influence of the size was researched.

The algorithm was based on the assumption that during gas leak some regions around the hole in the pipe are quickly losing temperature value due to rapid gas expansion. The change should be significant enough for even a low-cost thermal imaging cameras to be noticed. In order to estimate the average temperature of the pipe and notice potential changes in temperature multiple tests were performed. Each test was conducted twice. Once during summer and once during autumn.

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Fig. 5. The scene used for the tests of the algorithm. Image with an automatic

contrast enhancement.

In the fig. 6 the screenshots from one of the recorded sequences are visible. Every 5 minutes one screenshot was chosen to present the influence of the gas leak on the pipe temperature. It is clearly visible that there is some influence. However this type of influence could be a result of a sunny day, there is not a clear evidence recognizable by the human operator.

Fig. 6. Scene observation during the 15 minutes gas leak test. A) before gas

leak, B) after 5 minutes, C) after 10 minutes, D) after 15 minutes.

The implementation of the algorithm used during tests was divided into two parts. First pipe ROI detection was performed and multiple frames in order to check consistency of the pipe detection was computed. Regardless different starting conditions the pipe temperature is distinguishable enough to be detected using the proposed segmentation method (fig. 7). The height of the pipe visible in the image was chosen as 10 pixels.

Fig. 7. Successful pipe ROI detection using the proposed algorithm. Yellow arrows marks the height of the pipe. Red line marks the pipe. Pipe ROI is a

smallest rectangle that includes all the pipe (not shown).

The detected pipe is segmented out of the image using its bounding box. Pipes during and after leak for a human appear as a very similar or the same (fig. 8). At the presented regions the background modeling is performed with a very low learning factor in order to detect rapid changes and adapt with a slow speed changes that happen during the day. After modeling a background a simple thresholding of the current value and a modeled value is performed. The approach is very fast to compute therefore the overall speed of the algorithm is sufficient to implement in real time monitoring systems.

Fig. 8. Pipe ROI. A) at the beginning of the leak, B) after leak.

In the fig. 9. it was presented that the proposed approach

acquired promising results in the field of the contactless gas leaks detection. The leak was successfully detected. It can be observed the influence of the leak on the pipe’s temperature. It is clearly visible that the temperature is changing and the change is significant enough to be noticed by the proposed algorithm even by the low cost thermal imaging cameras. The processing is done in real time therefore the alarm about the leak can be fired very soon after the leak detection is possible.

Data acquired using the algorithm could be later offline processed in order to measure the leak size, estimate the location within the pipe and finally detect its reason. Whether it was a metal crack or by unintended person’s influence. Regardless the possibility of the later offline processing all the potential leaks detected and their location, time, size, and the internal algorithm processing images are logged along the alarm.

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Fig. 9. An illustration of the gas extension’s influence on the pipes

temperature. A) a beginning of the gas leak, B-D) the next time units after the leak appeared.

In the fig. 10 it was presented the result of the algorithm’s

implementation. The detected pipeline was marked with two red lines at the edges. If in the image there is a detected leak it is marked with a yellow bounding box enclosing the whole size of the leak. Due to possible division of the algorithm into two stages: pipe detection and leak detection the speed of the algorithms leaves most of the computation power for other implementation e.g. a surveillance monitoring of a trespassers.

Fig. 10. The result of the implementation of the proposed algorithm. Red lines

mark the detected pipe. Yellow box marks the detected leak.

CONCLUSIONS AND FUTURE WORK It was presented a contactless method for gas leaks

detection in the pump stations using the low cost thermal imaging cameras. The algorithm is divided into three steps: a preprocessing during the edges are detected, a pipe detection using Hough transform on the gradient image and the last step of a leak detection using temperature temporal gradient analysis. Due to the nature of the algorithm further practical

division is possible at the level of implementation. First the pipe is detected, next only the background model learning and thresholding is performed. The algorithms implementation is capable of performing in real time without human operator influence and presence. It is a novelty of a practical importance because of the high risk of manual gas leaks detection.

However the solution is not perfect. Automatic pipeline detection works only for the straight pipes that can be distinguished in the thermal imaging cameras. The proposed algorithm could be used a part of continuous pipes condition monitoring and at the same time leaks detection component. Due to the high speed of the processing a space for additional improvements and features is left. One of the possible additional features could be surveillance of the critical infrastructure area.

ACKNOWLEDGMENT This work has been supported by Ministry of Science and

Higher Education funds in the years 2010 - 2012 as development project OR00 0112 12.

REFERENCES [1] R.T. Collins, A. J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y.Tsin,

D. Tolliver, N. Enomoto, O. Hasegawa, P. Burt, L. Wixson, A system for Video Surveillance and Monitoring, 2000,

[2] D.B. Cist, A.E. Schutz, State of the art for pipe & leak detection, a low-cost GPR gas pipe & leak detector, Status assessment of a project in National Energy Technology Laboratory (2001),

[3] T. Shehab-Eldeen, O. Moselhi, Automated Inspection of Utility Pipes: A Solution Strategy for Data Management,

[4] J. Illingworth, J. Kittler, A Survey of the Hough Transform, Computer Vision Graphics and Image Processing, p. 87-116 (1988),

[5] J. Canny. A computational approach to edge detection, Pattern Analysis and Machine Intelligence, IEEE Transactions, p.679–698 (1986),

[6] S.K. Sinha, P. W. Fieguth, Automated detection of cracks in buried concrete pipe images, Automation in Construction, p. 47-57 (2006),

[7] T. Niemueller, Automatic Detection and Segmentation of Cracks in Underground Pipeline Images, (2006),

[8] L.R. Liang, C.G. Looney, Competitive fuzzy edge detection, Applied Soft Computing, p.123-137 (2003),

[9] Scharr, Hanno, 2000, Dissertation (in German), Optimal Operators in Digital Image Processing,

[10] D. H. Ballard, Generalizing the Hough Transform to Detect Arbitrary Shapes Pattern Recognition, 13(2):111-122, 1981.

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