Autonomus Landing System for Aircraft: A Deep Learning and Computer Vision Based Approach

Emre Şatır; Elif Demirtaş; Halim Ağdemir; Fatma Yıldız

Article Details

Autonomus Landing System for Aircraft: A Deep Learning and Computer Vision Based Approach

Authors:

Emre Şatır | Elif Demirtaş | Halim Ağdemir | Fatma Yıldız

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.15 Issue 3, pp.726-743

Document Type:

Article

Publication Date:

1/2025

Document Language:

Turkish

Document ID (Org No.):

2001356712

pISSN:

2536-4618

Keywords:

Abstract:

 In recent years, the need for safe parking and landing systems for manned and unmanned aerial vehicles has increased with the expansion of their usage areas. In this study, a YOLOv8-based object detection model is proposed to optimize aircraft landing processes and evaluate the safety of landing areas. The developed system uses image processing techniques and artificial intelligence algorithms to identify potential obstacles and analyze the suitability of landing areas. In the study, various data sets are used, and the generalization capability of the model is enhanced by data augmentation techniques. Experimental results show that the proposed system can identify safe landing zones for aircraft with high accuracy. This study makes an important contribution to support safe and autonomous aircraft landing processes at airports and urban air mobility applications.

 In recent years, the need for safe parking and landing systems for manned and unmanned aerial vehicles has increased with the expansion of their usage areas. In this study, a YOLOv8-based object detection model is proposed to optimize aircraft landing processes and evaluate the safety of landing areas. The developed system uses image processing techniques and artificial intelligence algorithms to identify potential obstacles and analyze the suitability of landing areas. In the study, various data sets are used, and the generalization capability of the model is enhanced by data augmentation techniques. Experimental results show that the proposed system can identify safe landing zones for aircraft with high accuracy. This study makes an important contribution to support safe and autonomous aircraft landing processes at airports and urban air mobility applications.

 In recent years, the need for safe parking and landing systems for manned and unmanned aerial vehicles has increased with the expansion of their usage areas. In this study, a YOLOv8-based object detection model is proposed to optimize aircraft landing processes and evaluate the safety of landing areas. The developed system uses image processing techniques and artificial intelligence algorithms to identify potential obstacles and analyze the suitability of landing areas. In the study, various data sets are used, and the generalization capability of the model is enhanced by data augmentation techniques. Experimental results show that the proposed system can identify safe landing zones for aircraft with high accuracy. This study makes an important contribution to support safe and autonomous aircraft landing processes at airports and urban air mobility applications.

 Son yıllarda, kullanım alanlarının genişlemesiyle birlikte, insanlı ve insansız hava araçları için güvenli park ve iniş sistemlerine duyulan ihtiyaç artmıştır. Bu çalışmada, hava araçlarının iniş süreçlerini optimize etmek ve iniş alanlarının güvenliğini değerlendirmek için YOLOv8 tabanlı bir nesne tespit modeli önerilmektedir. Geliştirilen sistem, görüntü işleme teknikleri ve yapay zeka algoritmalarını kullanarak potansiyel engelleri belirlemekte ve iniş alanlarının uygunluğunu analiz etmektedir. Çalışmada, çeşitli veri setleri kullanılmış ve veri artırma teknikleriyle modelin genelleme yeteneği güçlendirilmiştir. Deneysel sonuçlar, önerilen sistemin hava araçları için güvenli iniş bölgelerini yüksek doğrulukla belirleyebildiğini göstermektedir. Bu çalışma, havaalanlarında ve kentsel hava mobilitesi uygulamalarında güvenli ve otonom hava aracı iniş süreçlerini desteklemek için önemli bir katkı sunmaktadır.

Article Details

Autonomus Landing System for Aircraft: A Deep Learning and Computer Vision Based Approach

Authors:

Emre Şatır | Elif Demirtaş | Halim Ağdemir | Fatma Yıldız

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.15 Issue 3, pp.726-743

Document Type:

Article

Publication Date:

1/2025

Document Language:

Turkish

Document ID (Org No.):

2001356712

pISSN:

2536-4618

Object detection

Autonomous landing for aircraft

Deep learning

Image processing

Data augmentationNesne tespiti

Hava araçları için otonom iniş

Derin öğrenme

Görüntü işleme

Veri artırma

Issues

Article Details

Authors: Emre Şatır | Elif Demirtaş | Halim Ağdemir | Fatma Yıldız

Publisher: Igdir University

Main Topics: Bilgisayar Yazılımı

Journal: Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.15 Issue 3, pp.726-743

Document Type: Article

Publication Date: 1/2025

Document Language: Turkish

Document ID (Org No.): 2001356712

pISSN: 2536-4618

Issues

Authors:

Emre Şatır | Elif Demirtaş | Halim Ağdemir | Fatma Yıldız

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.15 Issue 3, pp.726-743

Document Type:

Article

Publication Date:

1/2025

Document Language:

Turkish

Document ID (Org No.):

2001356712

pISSN:

2536-4618