Helipad detection and classification for safe helicopter landing

• Autorzy: Mohammed Emad A. , Ali Ahmed J. , Abdullah Abdullah Muhammad

Identyfikatory

DOI

10.15199/48.2024.01.26

Warianty tytułu

PL

Wykrywanie i klasyfikacja lądowisk dla helikopterów w celu bezpiecznego lądowania helikoptera

• Języki publikacji: EN

Abstrakty

EN

One of the most important factor that plays a great role in the safty issue of the helicopter and the pilot is the response to system failure and pilot conciousness, so the helicopter must be delivered a safe landing point.in this reseach a helipad detection and recognition for helicopter autolanding have been presented , where the comparison tool was specifically used vgg19 and resnet50 CNN algorithms were used to classify safe helipads or occupied one to achieve autolanding, a dataset consisting of 4012 images divided into training and validation of the performance of the Resnet50 model and VGG19 where the accuracy was 94% VGG19 and Accuracy 96% Resnet50 was the best.

PL

Jednym z najważniejszych czynników odgrywających ogromną rolę w kwestii bezpieczeństwa śmigłowca i pilota jest reakcja na awarię systemu i świadomość pilota, dlatego śmigłowiec musi otrzymać bezpieczne miejsce lądowania. W tym badaniu wykrycie i rozpoznanie lądowiska dla helikopterów dla automatycznego lądowania helikoptera, gdzie specjalnie wykorzystano narzędzie porównawcze vgg19 i resnet50, zastosowano algorytmy CNN do klasyfikacji bezpiecznych lądowisk dla helikopterów lub zajętych w celu osiągnięcia automatycznego lądowania, zbiór danych składający się z 4012 obrazów podzielonych na trening i walidację działania modelu Resnet50 i VGG19, gdzie dokładność wyniosła 94%, VGG19 i dokładność 96% Resnet50 był najlepszy.

Słowa kluczowe

EN

vgg19; RESNET 50; helipad image detection

PL

VGG19; ResNet50; wykrywanie obrazu lądowiska dla helikopterów

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 1
• Strony: 127--129
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Mohammed Emad A.

• Northern Technical University, Engineering Technical College of Mosul, Department of Computer Engineering Technology, Iraq

autor

Ali Ahmed J.

• Northern Technical University, Engineering Technical College of Mosul, Department of Computer Engineering Technology, Iraq

autor

Abdullah Abdullah Muhammad

• Northern Technical University, Engineering Technical College of Mosul, Department of Computer Engineering Technology, Iraq

Bibliografia

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• [4] Laurent Muratet ,Stephane Doncieux , Yves Briere , Jean-Arcady Meyer, A Contribution to Vision-Based Autonomous Helicopter Flight in Urban Environments,journal,Robotics and autonomous systems,vol 50,31 march 2005,page 195-209.
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• [12] V. Sudha1 and T. R. Ganeshbabu, A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning, Computers, Materials & Continua, DOI:10.32604/cmc,2020.012008.
• [13] Bin Li , Dimas Lima b, Facial expression recognition via ResNet-50,journal, International Journal of Cognitive Computing in Engineering 2 (2021) 57–64.
• [14] Goru Uday Kiran , Garikapati Bindu , Kanegonda Ravi Chythanya and Kondru Ayyappa Swamy, Spiral Search Grasshopper Features Selection with VGG19-ResNet50 for Remote Sensing Object Detection Andrzej Stateczny ,journal, Remote Sens. 2022, 14, 5398.
• [15] Bishwas Mandal, Adaeze Okeukwu, Yihong Theis,Masked Face Recognition using ResNet-50,journal, arXiv:2104.08997v1 [cs.CV] 19 Apr 2021.
• [16] Bishwas Mandal, Adaeze Okeukwu, Yihong Theis, Masked Face Recognition using ResNet-50, arXiv:2104.08997v1 [cs.CV] 19 Apr 2021.
• [17] Sajja Tulasi Krishna, Hemantha Kumar Kalluri, Deep Learning and Transfer Learning Approaches for Image Classification,journal, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-5S4, February 2019.
• [18] Rahul Chauhan, Kamal Kumar Ghanshala, R.C Joshi, Convolutional Neural Network (CNN) for Image Detection and Recognition,First International Conference on Secure Cyber Computing and Communication (ICSCCC) 2018.
• [19] Huthaifa A. Ahmed, Emad A. Mohammed, Detection and Classification of The Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs), Iraqi Journal of Science, 2022, Vol. 63, No. 11, pp: 5058-5071.
• [20] A Z Mohammed , E A Mohammed and A M Aaref, Real-Time Driver Awareness Detection System,journal, Materials Science and Engineering 745 (2020) 012053.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).