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In this paper, the automated acquisition and processing of images of 100% of the surface of the tunnel is proposed that improves upon the results obtained with traditional techniques. The management of a large amount of information provided by the image sensors on the entire surface of the tunnel does not allow the manual evaluation of data, so new methods for the automated processing of this data have been developed. The proposed methods allow for the inspecting and detecting of fissures and cracks in the surface of the tunnel. This type of defect is one of the most usual, but also one of the most difficult to detect.
More specifically, the detection of cracks has been addressed in many areas, such as the inspection of concrete and asphalt infrastructures. One of the most developed fields is the detection of cracks in roads, these being one of the infrastructures with the largest surface anywhere in the world. The simplest and most common detection method is direct image segmentation, given that cracks are usually darker than the surrounding areas. Morphological filters are then applied to the segmented images in order to join the cracks and remove the segmentation noise, as is done for example in [1]. Other works have addressed a more elaborate segmentation, such as [12], where the Neighboring Difference Histogram Method (NDHM), which compares each pixel with the histogram of the surrounding pixels, is used; or [6], where the images are divided into different zones, to which a modified Otsu thresholding algorithm is applied [13], and the resulting entropy is computed. Other methods are based on the search for seeds that are merged by taking into account the fact that the cracks appear as elongated structures [14]. Recently, Convolutional Neuronal Networks has been used to detect cracks, as can be found in [15], where cracks are detected at pixel level.
A significant number of works can also be found where the detection of cracks in concrete buildings and infrastructures, especially bridges, is addressed. Four methods for the detection of cracks in concrete bridges are compared in [16]: Sobel and Canny edge detectors, the Fourier transform, and the Haar wavelet transform. These authors conclude that the Haar wavelet transform is the best performer.
A method for detecting cracks in concrete structures is described in [17]. This method is based on the application of a large number of filters to a database that has previously been processed by hand. The optimal combination of filters to obtain the best possible result is selected using genetic algorithms. In [10], a method for detecting cracks in concrete surfaces by areascan color sensors, based on different edge detection algorithms, is described. The system can detect cracks with an opening greater than 0.25 mm.
The works where cracks are detected inside tunnels differ from the others mainly due to the use of artificial light. Some works are based on thresholding techniques, such as [4], where the value of each pixel is compared to the average value of the pixels in the column where that pixel is located. The work of Fujita et al. [11], cited above, can be included in this group. The main drawback of these techniques is how to choose the threshold value. The methods for setting the threshold value are usually based on prior knowledge, but these methods are not generalizable and do not usually work properly when applied to real tunnels, where shadows and humidity may occur.
Other techniques for detecting cracks in tunnels are based on models, such as the one described above in [3], using the concept of percolation, or the one presented in [18], where it is assumed that cracks are formed by line segments joined together. In the second case, the image resolution is 2.5 mm per pixel and can be considered a semi-automatic method, as it needs human intervention to choose the seeds of every crack.
There are also some methods based on pattern recognition, such as the one proposed in [19], where the SVM (Support Vector Machine) algorithm is used to determine whether the preselected areas are really cracks. An inspection system of concrete tunnels composed of nine linear color CMOS cameras, whose images are transformed to gray images for processing, is proposed in [8]. First, a smoothing filter to remove noise is applied to the gray images. The resulting image is filtered by the top-hat filter to enhance cracks, as they are supposed to be darker than the rest of the image. The threshold for segmenting the filtered image is set by analyzing the distribution of the gray levels of the filtered image. Neighboring pixels are grouped, and shape and gray level features are calculated to determine which groups are really cracks and which are not. For this, four pattern recognition techniques are applied: two neural networks, the SVM algorithm, and the nearest neighbor algorithm. The accuracy of these algorithms is approximately 90%, which was calculated using 200 pixel groups, of which 64 were actually cracks. A total of nine linear cameras of 12,288 pixels acquire images in a subway tunnel covering 270 of the tunnel, but the tunnel diameter was not available, so the image resolution cannot be determined.
Table 1 summarizes the described related work. However, in general, the reviewed techniques are often not suitable for the detection of cracks in tunnels. The roughness of the concrete, the shadows derived from this roughness, and the fact that the crack brightness is often similar to that of the surrounding areas mean that these segmentation techniques can hardly provide the required results.
Concerning the mechanical defect type, there are fissures that appear in the surface mainly due to the presence of tensions over its resistance capacity. When a fissure traverses the entire element thickness, from side to side, it becomes a crack. Other mechanical defects are detachments or ruptures on precasts, broken off corners, or seen steel frames. Some examples of these defects are shown in Figure 1. Finally, other defects are of a chemical nature, such as carbonation, efflorescence, and other defects related to the presence of water, such as moisture, dripping, and seepage.
This paper aims to develop a system for the detection of fissures and cracks in tunnels. This type of defect can be identified through visual inspection, but often goes unnoticed during a routine human inspection. In addition, these defects appear at preliminary phases of tunnel deterioration, so their early detection allows preventive actions to be taken, before the problem becomes more severe. Moreover, both fissures and cracks exhibit a similar visual appearance. Therefore, from now on, we will use the term crack for referring to both fissures and cracks.
Concerning camera sensor resolution, common linear cameras range from about 1024 pixels for an e2v AviiVA SM2 1010, for example, to 12,288 pixels for an e2v AviiVA UM8 camera. The number of cameras required for inspection is determined by the actual sensor resolution and the minimum crack size:
In this expression, NC is the number of cameras, PT is the tunnel perimeter, WCmin is the minimum crack width, and NP is the number of pixels of the camera sensor. Moreover, a (small) overlap between the field of view of two adjacent cameras is recommended to avoid gaps.
The necessary sensor resolution that allows the camera to measure these types of crack requires twice the resolution of the image, i.e., to detect cracks 1 mm wide, images with a resolution of at least 2 pixels per millimeter should be acquired. Typical minimum crack values are between 0.1 and 2 mm, which would include most types of cracking defects and cracks in tunnels.
Table 3 reports some examples of sensor camera resolution, number of cameras and focal length of the optics recommended for detecting cracks ranging from 0.1 to 2 mm, which cover most expected crack types, for three tunnel types: electricity tunnel, underground tunnel and high-speed train tunnel (sized 3.5 m, 5.3 m, and 10.40 m in diameter, respectively). Both one-pass and two-pass acquisition cases are shown.
where SMAX is the maximum admissible speed of the vehicle, RA is the resolution in the direction of movement of the vehicle, TExp is the exposure time, and WCmin is the minimum crack width. A correction factor of 0.9 is then applied to the result in order to ensure that the maximum speed is never exceeded. For example, if exposure time is set to 100 µs and WCmin is 0.2 mm, it results in SMAX being 1 m/s. Appling the correction factor means that the vehicle speed should never be greater than 0.9 m/s.
A comparison of the obtained results with those obtained by other authors can be approached by taking into account the fact that, currently, there are no open datasheets available about tunnel cracks, and even the way to evaluate the results is not always the same. Some works use Gabor filters for the detection of cracks, as in [20,25]. In these works, instead of analyzing the result at pixel level, the images are divided into portions, and the detection of cracks is approached in each portion. A balanced accuracy of about 90% is reported in these works, to which the 95% obtained in this work compares favorably with [20,25]. Other works used Convolutional Neural Networks for the detection of cracks at pixel level, as can be found in [15]. The sensitivity obtained in [15] is 92.5%, while precision is 87.0%, which compares favorably to the 95.3% and 98.8%, respectively, obtained in the present paper. In the case of crack detection in the concrete surface of the tunnel, as in [8], the authors use different processing techniques and compare them to each other. The images are captured in a subway tunnel, and cracks wider than 0.3 mm and longer than 15 cm can be detected. The balanced accuracy value is not computed in this work, but the reported accuracy value, which is always equal to or greater than the balanced accuracy, ranges from 88.7% to 91.7%. Therefore, our method would compare favorably to these works. 2ff7e9595c
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