Trial of shaft inspection by drone
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'Drone Ground Control Station' is a system for controlling and monitoring Unmanned Aerial Vehicle (UAV) or Drone from any remote place. In existing nonmilitary drone ground control stations, the software can only present the telemetry data and control parameters from drone, which do not ensure the safety of an on flight drone from crashing due to bad weather and other facts. Moreover, the software and the control unit of these ground control stations are not customizable. This paper is about a drone control station which incorporates a hardware system for establishing secured communication with drone and a software for presenting the telemetry data as well as the important weather parameters and no fly zones of a region in which the drone will fly. A microcontroller is used in the hardware unit for central controlling of the ground control station. It is possible to interface any customized physical control unit like joystick with the ground control station software. The communication between drone and the ground station is established by MAVLink, a widely used communication protocol for drone. The proposed design of the system was tested by several dummy and real test flights of drone, which makes it reliable for using in any nonmilitary drone operations.
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Joystick
Ground station
Control unit
Interface (matter)
Remote Control
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This paper presents a case of filming a bridge in Novi Sad during its construction for the purpose of inspection of construction works carried out by the Spanish - Italian consortium AZVI. Twice a month the photographs and high-resolution films shot by a drone were sent to AZVI. In this way, the contractors had up-to-date information on the progress of the works without the need for a permanent presence of the company's engineers. Drone shooting has enabled high-resolution images and movies which allow quality remote inspection, accompanied, naturally, with occasional arrivals at the site.
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Bridge (graph theory)
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Deep learning combined with autonomous drones is increasingly seen as an enabler of automated aircraft inspection which can support engineers detect and classify a wide range of defects. This can help increase the accuracy of damage detection, reduce aircraft downtime, and help prevent inspection accidents. However, a key challenge in neural networks is that their stability is not yet well understood mainly due to the large number of dimensions and the complexity of their shapes. This paper illustrates this challenge through a use case that applies MASK R-CNN to detect aircraft dents. The results show that environmental factors such as raindrops can lead to false positives. The paper also proposes various test scenarios that need to be considered by the developers of the drone-based inspection concept to increase its reliability.
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