**3.2 Post detection**

The work of the system relies on an accurate generation of trigger signals at a specific distance relative to each catenary post. As a result, a reliable post detection module is very important in the system. The post detection module should be adaptive to different detection environments and types of catenary.

In order to achieve a reliable detection, several laser distance sensors are used which are mounted upward. The range of distance between the sensors and steady

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expected.

**Figure 6.** *Camera system.*

*Automatic Visual Inspection and Condition-Based Maintenance for Catenary*

**Camera resolution (megapixel)**

Cantilever 29 14 Forward and backward

Additional wire 16 4 Forward and backward Hanging post 16 4 Forward and backward

Contact wire 25 2 Left and right

Catenary video 4 1 Forward

**Number of cameras**

**View directions**

arms is utilized as the criterion in the data analysis of post detection. Measurement data are captured and analyzed in real time. The trigger signals are generated and

Laser sensors in the 1# group are used when the inspection car runs forward, and those in the 2# group are used when the inspection car runs backward. Finally, an accurate photographic distance at each post can be achieved for each camera as

In the camera system, up to 25 super-high-resolution area-scan cameras are used

Considering the great data storm at each post, high-performance servers are used in the system. The architecture of multi-thread processing is shown in **Figure 8**. The tasks of image acquisition, image compression, and image saving are distributed in different threads for each camera and are performed based on the technologies of

to capture image data of each catenary component. In the maximum inspection speed of 160 km/h, the overall original image data is up to 10 Gb/s**!** It is a great chal-

lenge for the image acquisition, compression, and data storage.

The principle of the post detection module is illustrated in **Figure 7**.

distributed to each camera and strobe light.

**3.3 Image acquisition, processing, and saving**

parallel computing and GPU acceleration.

*DOI: http://dx.doi.org/10.5772/intechopen.82149*

**Detection modules**

**Table 1.** *Camera system.*


#### *Automatic Visual Inspection and Condition-Based Maintenance for Catenary DOI: http://dx.doi.org/10.5772/intechopen.82149*

#### **Table 1.**

*Maintenance Management*

**3. Methodology**

**Figure 5.** *System architecture.*

**3.1 Camera system**

megapixel.

light are applied in the system.

**3.2 Post detection**

to perform image detection of each component.

Catenary components to be checked are mounted in a large area. It covers [−3500, 3500 mm] in the horizontal direction and [4800, 8100 mm] in the vertical direction relative to the track center. High-resolution image data are needed in order

In our system, the candidate detection area is divided into several detection regions. A dedicated designed camera system is mounted on an inspection car, which is composed of many area-scan cameras with different directions and fields of view. Super-high-resolution cameras are used which have a resolution up to 29

**Table 1** gives details of the camera system. The camera system is carefully designed so that it covers almost all the components to be checked with an image resolution up to 0.5 mm/pixel, which gives great details of each component.

**Figure 6** illustrates the design for part of the camera system.

tive to different detection environments and types of catenary.

In order to get a brighter light and better image quality at night, groups of strobe

The work of the system relies on an accurate generation of trigger signals at a specific distance relative to each catenary post. As a result, a reliable post detection module is very important in the system. The post detection module should be adap-

In order to achieve a reliable detection, several laser distance sensors are used which are mounted upward. The range of distance between the sensors and steady

**140**

*Camera system.*

**Figure 6.** *Camera system.*

arms is utilized as the criterion in the data analysis of post detection. Measurement data are captured and analyzed in real time. The trigger signals are generated and distributed to each camera and strobe light.

The principle of the post detection module is illustrated in **Figure 7**.

Laser sensors in the 1# group are used when the inspection car runs forward, and those in the 2# group are used when the inspection car runs backward. Finally, an accurate photographic distance at each post can be achieved for each camera as expected.

#### **3.3 Image acquisition, processing, and saving**

In the camera system, up to 25 super-high-resolution area-scan cameras are used to capture image data of each catenary component. In the maximum inspection speed of 160 km/h, the overall original image data is up to 10 Gb/s**!** It is a great challenge for the image acquisition, compression, and data storage.

Considering the great data storm at each post, high-performance servers are used in the system. The architecture of multi-thread processing is shown in **Figure 8**. The tasks of image acquisition, image compression, and image saving are distributed in different threads for each camera and are performed based on the technologies of parallel computing and GPU acceleration.

**Figure 7.**

#### **Figure 8.**

*Multi-thread processing.*

Although the huge amount of image data are captured and compressed on several acquisition platforms, the final compressed JPEG images are saved in a common data server, as illustrated in **Figure 9**.

There are labels in the name of each image file, which indicates the camera, post number, detection region, and frame number. The image data are well aligned, and all the image data for the same catenary post are saved in a common file folder, which is helpful for the data management and analysis.

#### **3.4 Image detection**

Technologies of visual inspection and image detection have already been used in the detection of track components such as fasteners and track slabs [7, 8]. However, it is quite different for the detection of catenary components. Not only the number of components and defect types to be checked is much larger, but also catenary components vary greatly in size and location, which brings more difficulties in the image detection.

In this paper, we proposed an intelligent analysis framework. Firstly, as shown in **Figures 10** and **11**, structural analysis is applied in order to localize each component in the image. Technologies of matching are exploited here, based on pair-wise constraints [9] between the main catenary components such as steady arm, registration arm, cantilever tube, etc.

Secondly, according to the analysis on the characteristics of different components and typical defects, elaborate detection algorithms are separately developed for each component, based on different features such as geometry, texture, and logic rules (**Figures 12–15**).

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**Figure 12.**

efficiency (**Figure 16**).

*Detection based on geometry feature.*

*Automatic Visual Inspection and Condition-Based Maintenance for Catenary*

Finally, only a few of candidate defects are automatically selected and need to be manually rechecked by the system operator, which greatly improves the inspection

*DOI: http://dx.doi.org/10.5772/intechopen.82149*

**Figure 9.** *Data storage.*

**Figure 10.** *Structural analysis.*

**Figure 11.** *Structural analysis.* *Automatic Visual Inspection and Condition-Based Maintenance for Catenary DOI: http://dx.doi.org/10.5772/intechopen.82149*

**Figure 9.** *Data storage.*

*Maintenance Management*

Although the huge amount of image data are captured and compressed on several acquisition platforms, the final compressed JPEG images are saved in a common

There are labels in the name of each image file, which indicates the camera, post

Technologies of visual inspection and image detection have already been used in the detection of track components such as fasteners and track slabs [7, 8]. However, it is quite different for the detection of catenary components. Not only the number of components and defect types to be checked is much larger, but also catenary components vary greatly in size and location, which brings more difficulties in the image detection. In this paper, we proposed an intelligent analysis framework. Firstly, as shown in **Figures 10** and **11**, structural analysis is applied in order to localize each component in the image. Technologies of matching are exploited here, based on pair-wise constraints [9] between the main catenary components such as steady arm, registration

Secondly, according to the analysis on the characteristics of different components and typical defects, elaborate detection algorithms are separately developed for each component, based on different features such as geometry, texture, and

number, detection region, and frame number. The image data are well aligned, and all the image data for the same catenary post are saved in a common file folder,

data server, as illustrated in **Figure 9**.

**3.4 Image detection**

**Figure 8.**

**Figure 7.** *Post detection.*

*Multi-thread processing.*

arm, cantilever tube, etc.

logic rules (**Figures 12–15**).

which is helpful for the data management and analysis.

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**Figure 10.** *Structural analysis.*

**Figure 11.** *Structural analysis.*

Finally, only a few of candidate defects are automatically selected and need to be manually rechecked by the system operator, which greatly improves the inspection efficiency (**Figure 16**).

**Figure 13.** *Detection based on geometry feature.*

**Figure 14.** *Detection based on texture feature.*

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**Figure 17.**

*Equipment on the inspection car.*

**4. Applications**

*Manual rechecking for candidate defects.*

**Figure 16.**

that we are interested in.

tion equipment on one of the inspection car.

enough to perform the subsequent detection.

*Automatic Visual Inspection and Condition-Based Maintenance for Catenary*

The proposed system has been installed on several catenary inspection cars which have a maximum inspection speed of 160 km/h. **Figure 17** shows the inspec-

The graphic user interface is shown in **Figure 18**. All the camera control and image display of different cameras can be done in the common software, and we can also enlarge a specific part of image to see much detail of any component or region

The camera system is designed with an image resolution up to 0.5 mm/pixel. **Figures 23–25** show details of some detection regions which are enlarged from the original images. As shown below, the images of catenary components are clear

Images of different inspection regions are shown in **Figures 19–22**.

*DOI: http://dx.doi.org/10.5772/intechopen.82149*

**Figure 15.** *Detection based on texture feature.*

*Automatic Visual Inspection and Condition-Based Maintenance for Catenary DOI: http://dx.doi.org/10.5772/intechopen.82149*


**Figure 16.**

*Maintenance Management*

**Figure 13.**

**Figure 14.**

*Detection based on texture feature.*

*Detection based on texture feature.*

*Detection based on geometry feature.*

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**Figure 15.**

*Manual rechecking for candidate defects.*
