دورية أكاديمية
An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
العنوان: | An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing |
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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 |
تدمد: | 20763417 |
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DOI: | 10.3390/app14135464 |