**3. Tower detection process**

The transmission towers are important elements of the electric transmission system. They require maintenance of its components such as the isolators and the power line connections.

The tower detection process is done mainly using computer vision techniques based in machine learning methods such as neural networks, SVM [19, 20] and recently deep learning [1]. The process requires a training stage. In this case a classifier is trained using a set of labelled images. A manual tool is used for labelling, this is for selecting the region of interest (ROI) where the tower is located as shown in **Figure 9**. In the selected ROI, a set of descriptors is extracted. The descriptors are the input to the classifier. After that, a detection stage operates with frames of videos. The linear information of the scene can be obtained using line detection methods. This can be useful to simplify the scene. The tower detection process is composed of two stages, training and detection, as shown in **Figure 10**. The result of the tower detection process is in **Figure 11**.

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also to avoid collisions with them.

*Tower detection examples using SVM and a grid of descriptors.*

*Training and detection stages for tower detection.*

**4. Autonomous navigation process**

computation [1].

**Figure 10.**

**Figure 11.**

*Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure…*

For an autonomous inspection system based on UAV, the tower can be a distinctive element for the navigation process. However, it is an element that may be at risk of collision. Autonomous systems must be prepared to use towers as a reference area and

The recent advances in computer vision methods for object detection towards

onboard computers provided with GPU (graphics processing unit) for accelerating

Vision-based autonomous navigation for UAVs is a complex process that requires short computing times and accurate measurements in order to provide suitable and safe control commands to the device. The UAV navigation requires real-time measurements to produce a response within a specified time (at least 100 ms); otherwise, severe consequences including failure may affect the device. The simulation of a control system for a fixed-wing UAV that uses vision-based navigation for power line tracking is presented in [21]. In a previous work, the

the use of deep learning methods. These algorithms can be implemented on

*DOI: http://dx.doi.org/10.5772/intechopen.86689*

**Figure 9.** *The ROI selection of an electrical tower.*

*Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure… DOI: http://dx.doi.org/10.5772/intechopen.86689*

**Figure 10.** *Training and detection stages for tower detection.*

*Path Planning for Autonomous Vehicles - Ensuring Reliable Driverless Navigation...*

generated when a flexible cable is suspended between two poles or towers. This type of object appears in images of electrical infrastructure taken from non-top-down views, which could be obtained using manned aircraft or UAVs. Different methods for catenary detection that includes the use of matching filters, line segment pool and a graph-cut model as is shown in [17] exist, also using geometrical considerations and data structures of segment concatenation [18]. The results of catenary

The transmission towers are important elements of the electric transmission system. They require maintenance of its components such as the isolators and the

The tower detection process is done mainly using computer vision techniques based in machine learning methods such as neural networks, SVM [19, 20] and recently deep learning [1]. The process requires a training stage. In this case a classifier is trained using a set of labelled images. A manual tool is used for labelling, this is for selecting the region of interest (ROI) where the tower is located as shown in **Figure 9**. In the selected ROI, a set of descriptors is extracted. The descriptors are the input to the classifier. After that, a detection stage operates with frames of videos. The linear information of the scene can be obtained using line detection methods. This can be useful to simplify the scene. The tower detection process is composed of two stages, training and detection, as shown in **Figure 10**. The result

detection based on a segment concatenation are shown in **Figure 8**.

**56**

**Figure 9.**

*The ROI selection of an electrical tower.*

**3. Tower detection process**

*Catenary detection based in a segment concatenation.*

of the tower detection process is in **Figure 11**.

power line connections.

**Figure 8.**

**Figure 11.** *Tower detection examples using SVM and a grid of descriptors.*

For an autonomous inspection system based on UAV, the tower can be a distinctive element for the navigation process. However, it is an element that may be at risk of collision. Autonomous systems must be prepared to use towers as a reference area and also to avoid collisions with them.

The recent advances in computer vision methods for object detection towards the use of deep learning methods. These algorithms can be implemented on onboard computers provided with GPU (graphics processing unit) for accelerating computation [1].

## **4. Autonomous navigation process**

Vision-based autonomous navigation for UAVs is a complex process that requires short computing times and accurate measurements in order to provide suitable and safe control commands to the device. The UAV navigation requires real-time measurements to produce a response within a specified time (at least 100 ms); otherwise, severe consequences including failure may affect the device. The simulation of a control system for a fixed-wing UAV that uses vision-based navigation for power line tracking is presented in [21]. In a previous work, the

**Figure 12.** *Simulation of autonomous navigation.*

**Figure 13.** *UAV system.*

**59**

**Figure 15.**

*Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure…*

simulation of a visual-based navigation process for power line following in a 3D

Two pictures of the simulator of autonomous navigation using power line detec-

This camera provided images that are suitable for the line detection process. The frame rate permits closed loop control. The UAV system consists of a set of related components that are shown in **Figure 13**. The main components of the system are the UAV flight platform; the flight controller; the sensors that include GPS and inertial measurement unit (IMU) that includes three-axis magnetometers, gyroscopes, accelerometers and compass; camera; and the onboard computer to run the developed software. In this system, the flight controller receives setpoints from the onboard computer and sends sensed information to it. The vision sensor (camera)

Different kinds of missions for power line following and terrain inspection can

Effective and efficient generation of 3D models from a set of 2D images is a wellstudied problem in the literature and the principle of numerous computer vision applications. The keypoint detection and the 2D descriptor extraction are the first steps in the reconstruction process followed by the matching. There are different 2D descriptors such as SIFT, ORB, BRISK and FREAK that can be used in the context of 3D reconstruction using structure from motion (SFM). From the study [3], it can be concluded that it is possible to use the aforementioned descriptors in electrical tower reconstruction context. Also, the results shown that the SIFT descriptor presents the best performance in the generated cloud of points, but it spends more time than using other descriptors. Another good option is the use of the ORB descriptor.

Current developments tend towards the use of other types of sensors such as LIDAR whose information can be merged with information from cameras with

Also it is important to develop an online process of object recognition by using simultaneous localization and mapping (SLAM). This can help to improve the object detection stage in order to obtain a more robust navigation system.

be established. The main stages of a complete mission are shown in **Figure 14**.

environment using a closed loop control was presented in [22].

*DOI: http://dx.doi.org/10.5772/intechopen.86689*

sends frames of images to the onboard computer.

In **Figure 15**, a result using SIFT is presented.

*Results of 3D reconstruction of electrical infrastructure.*

different spectra.

**5. 3D reconstruction of electrical infrastructure**

tion are shown in **Figure 12**.

**Figure 14.** *Autonomous mission process stages.*

*Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure… DOI: http://dx.doi.org/10.5772/intechopen.86689*

simulation of a visual-based navigation process for power line following in a 3D environment using a closed loop control was presented in [22].

Two pictures of the simulator of autonomous navigation using power line detection are shown in **Figure 12**.

This camera provided images that are suitable for the line detection process. The frame rate permits closed loop control. The UAV system consists of a set of related components that are shown in **Figure 13**. The main components of the system are the UAV flight platform; the flight controller; the sensors that include GPS and inertial measurement unit (IMU) that includes three-axis magnetometers, gyroscopes, accelerometers and compass; camera; and the onboard computer to run the developed software. In this system, the flight controller receives setpoints from the onboard computer and sends sensed information to it. The vision sensor (camera) sends frames of images to the onboard computer.

Different kinds of missions for power line following and terrain inspection can be established. The main stages of a complete mission are shown in **Figure 14**.
