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All Journal Bulletin of Electrical Engineering and Informatics SEEIJ (Social Economics and Ecology International Journal)
Dany Eka Saputra
Bina Nusantara University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Electrical & Electronics Engineering Social Sciences Other

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Digitalization of Drone Monitoring Process on Indonesia Ministry of Transportation Dany Eka Saputra
Social Economics and Ecology International Journal (SEEIJ) Vol. 4 No. 1 (2020): March
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/seeij.v4i1.7351

Abstract

Drone has been used for various commercial activity due to its flexibility and cost efficiency. However, to ensure that each drone operation still obey safety regulation, each drone activity must be monitored and heavily regulated. The administrative process to fly a drone was cost and time consuming. All process must be conducted manually in Jakarta. This activity was conducted as part of consultancy for Directorate of Flight Navigation, Ministry of Transportation. The aim of this activity was to digitize the administrative process of drone operation. The result was a digitalization plan, which consist softwares needed for each administrative process, and implementation plan, which recommend the time and order of implementation.
Comparison between convolutional neural network and K-nearest neighbours object detection for autonomous drone Annisa Istiqomah Arrahmah; Rissa Rahmania; Dany Eka Saputra
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3784

Abstract

In autonomous drones, the drone’s ability to move depends on the drone’s capacity to know its position, either in relative or absolute position. The Pinhole model is one of the methods to calculate a drone’s relative position based on the triangle similarity concept using a single camera. This method utilizes bounding box information generated from an object detection algorithm. Thus, accuracy of the generated bounding box is crucial, and selection of object detection algorithm is necessary. This paper compares and evaluates machine learning and deep learning object detection methods to determine which method is suitable for distance measurement using a single camera for autonomous drone’s controller based on pinhole model. A novel K-nearest neighbours-based (KNN-based) object detection is constructed to represent the machine learning method while you only look once version 5 (YOLOv5) convolutional neural network (CNN) architecture is selected to represent the deep learning method. A dataset consists of two different classes, with a total of 1520 images, collected from the unmanned aerial vehicle (UAV) camera for training and evaluation purposes. Confusion matrix and intersection over union (IoU)/generalized intersection of union (GIoU) matrix are used to evaluate the performance of both methods. The result of this paper shows the performance of each system and concludes the suitable type of object detection algorithm for the autonomous UAV purpose.
Co-Authors Annisa Istiqomah Arrahmah Rissa Rahmania