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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 34, Number 7(2) (2022)
Copyright(C) MYU K.K.
pp. 2637-2661
S&M2996 Research Paper of Special Issue
Published in advance: June 30, 2022
Published: July 14, 2022

Positioning Errors of Objects Measured by Convolution Neural Network in Unmanned Aerial Vehicle Images [PDF]

Woosuk Kang, Jisung Kim, Hongsic Yun, Pooreum Lee, and Heecheol Kim

(Received April 9, 2022; Accepted June 21, 2022)

Keywords: aerial photogrammetry, convolution neural network (CNN), object detection, positioning error, unmanned aerial vehicle

The conversion of unmanned aerial vehicles (UAVs, also called drones) and convolution neural network (CNN) facilitates the location of objects in real time using their sensors. In photogrammetry, the positional accuracy of objects is directly affected by the use of technology. It is necessary to improve the accuracy of object positioning to increase the utilization of drones and CNNs. In this study, the error factors that impede accuracy, such as the global navigation satellite system (GNSS) error of the UAV, camera distortion error, and camera posture error, were analyzed to improve the accuracy of object positioning. The effect of each error was also analyzed. The study was conducted in stages, such as establishing a method for the positioning of objects, specifying errors, and analyzing the amount and effect of error elements. The magnitude of the positioning errors was found by comparing it with accurate values measured by GNSS. Furthermore, the correlation of the errors with the factors that impeded accuracy was analyzed. Consequently, the effect of each error factor on the overall error was identified. These results can play an important role in improving positioning accuracy and developing UAV and CNN technologies employing sensors in the future.

Corresponding author: Jisung Kim

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Cite this article
Woosuk Kang, Jisung Kim, Hongsic Yun, Pooreum Lee, and Heecheol Kim, Positioning Errors of Objects Measured by Convolution Neural Network in Unmanned Aerial Vehicle Images, Sens. Mater., Vol. 34, No. 7, 2022, p. 2637-2661.

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