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An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing

التفاصيل البيبلوغرافية
العنوان: An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
المؤلفون: Chenliang Li, Yunyang Wang, Yan Zhao, Cheng Yuan, Ruien Mao, Pin Lyu
المصدر: Applied Sciences, Vol 14, Iss 13, p 5464 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: carrier-based unmanned aerial vehicles, deep learning, runway detection, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Carrier-based Unmanned Aerial Vehicle (CUAV) landing is an extremely critical link in the overall chain of CUAV operations on ships. Vision-based landing location methods have advantages such as low cost and high accuracy. However, when an aircraft carrier is at sea, it may encounter complex weather conditions such as haze, which could lead to vision-based landing failures. This paper proposes a runway line recognition and localization method based on haze removal enhancement to solve this problem. Firstly, a haze removal algorithm using a multi-mechanism, multi-architecture network model is introduced. Compared with traditional algorithms, the proposed model not only consumes less GPU memory but also achieves superior image restoration results. Based on this, We employed the random sample consensus method to reduce the error in runway line localization. Additionally, extensive experiments conducted in the Airsim simulation environment have shown that our pipeline effectively addresses the issue of decreased detection accuracy of runway line detection algorithms in haze maritime conditions, improving the runway line localization accuracy by approximately 85%.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/14/13/5464; https://doaj.org/toc/2076-3417
DOI: 10.3390/app14135464
URL الوصول: https://doaj.org/article/29e0a3feeba84ba18c55158adfd5be27
رقم الأكسشن: edsdoj.29e0a3feeba84ba18c55158adfd5be27
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app14135464