**6. Conclusions**

Real-time power line detection is a challenging problem that must be performed using different methods such as edge detectors, machine learning methods and 3D computer vision. The main problems inside this are the image correction due the abrupt movements of the UAV, the difference of backgrounds found while flying and illumination changes. The tower detection using deep learning methods is recommended for a robust detection. The proposed vision strategy could help monitor the environment of power lines in order to prepare preventive maintenance for reducing risk of tree branches that can affect the electrical infrastructure.

As future work, a technique based on SLAM could be useful to deal with complex scenes in order to improve the process and extract 3D information as an online process using an onboard computer.

It is mandatory to focus the future work in collision avoidance systems that allow to protect both UAV and electrical infrastructure in order to minimize the risk of damages during inspection process or autonomous navigation. Motion prediction is necessary for path planning in autonomous systems, and risk assessment for intelligent vehicles is fundamental to improve the safety.

The development of a system with a fixed wing platform could be useful for long-distance inspections. Finally, the study of the effects of oscillation of the detected angle between the UAV and the power lines can be considered in order to improve the control strategy using methods such as filtering.
