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All Journal International Journal of Aviation Science and Engineering Journal of Applied Science, Engineering and Technology (J. ASET)
Widyosekti, M. Dhiku
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Aircraft Detection in Low Visibility Condition Using Artificial Intelligence Ummah, Khairul; Widyosekti, M. Dhiku; Arif, Yanuar Zulardiansyah; Saputra, Rizal Adi; Riszal, Akhmad; Sembiring, Javensius
International Journal of Aviation Science and Engineering - AVIA Vol. 5 No. 1: (June 2023)
Publisher : FTMD Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v5i1.84

Abstract

Bad weather often interferes with the functioning of the air transport system. One example is the frequent flight delays for commercial aircraft, resulting in losses for both the airline and passengers. Artificial Intelligence (AI) technology can now minimize delays caused by bad weather, especially in low visibility conditions. This paper discusses AI modeling that can detect aircraft in a low visibility weather condition, especially in the airport area. The employed method is the deep learning approach with the YOLOv4 algorithm (single-stage detection), which is regarded as one of the optimal platforms in this field. There are 600 images used in this work to create and train three different models. Image Dehazing filter is employed on the training data before it is trained to produce the detection model. The result shows that the model has a good performance in terms of performance metrices. Thus, this model is suitable to be used to detect aircraft in low visibility conditions.
Aircraft Detection in Low Visibility Condition Using Artificial Intelligence Sembiring, Javensius; Ummah , Khairul; Widyosekti, M. Dhiku; Arif, Yanuar Zulardiansyah; Huda, Zulmiftah
Journal of Applied Science, Engineering and Technology Vol. 4 No. 1 (2024): June 2024
Publisher : INSTEP Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/jaset.v4i1.64

Abstract

Bad weather often interferes with the functioning of the air transport system. One example is the frequent flight delays for commercial aircraft, resulting in losses for both the airline and passengers. Artificial Intelligence (AI) technology can now minimize delays caused by bad weather, especially in low visibility conditions. This paper discusses AI modeling that can detect aircraft in a low visibility weather condition, especially in the airport area. The employed method is the deep learning approach with the YOLOv4 algorithm (single-stage detection), which is regarded as one of the optimal platforms in this field. There are 600 images used in this work to create and train three different models. Image Dehazing filter is employed on the training data before it is trained to produce the detection model. The result shows that the model has a good performance in terms of performance metrices. Thus, this model is suitable to be used to detect aircraft in low visibility conditions.
Co-Authors Arif, Yanuar Zulardiansyah Huda, Zulmiftah Javensius Sembiring, Javensius Khairul Ummah Riszal, Akhmad Rizal Adi Saputra Ummah , Khairul