Authors: Matija Radovic, Offei Adarkwa and Qiaosong Wang
 
Journal Title: Journal of Imaging
 
ISSN: 2313-433X (Online)
 
Publisher: MDPI AG
 
Abstract
 
There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters.
 
This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.
 

Photo: Training set of object class “Airplane” (credits: Matija Radovic, Offei Adarkwa and Qiaosong Wang)

Photo: Testing set of object class “Airplane” (credits: Matija Radovic, Offei Adarkwa and Qiaosong Wang)

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
 

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