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- Title
An Improved CenterNet Method for Wing Icing Detection.
- Authors
WANG Yifan; WEI Jiatian; ZUO Chenglin; ZHOU Wenjun; XIONG Hao; ZHAO Rong; PENG Bo; WANG Yang
- Abstract
Aircraft wing icing detection is a crucial task during high-altitude flights because ice accumulation on the leading edge of wings can change their aerodynamic shape and reduce lift capacity. This paper proposes a rotated object detection method called RA-CenterNet, based on the CenterNet model, to overcome the limitations of existing icing detection approaches that either rely on operator experience or require high engineering implementation and hardware development costs. To address the specific icing area directions presented in wind tunnel experimental datasets, a novel angle prediction branch network that enables precise calibration of rotated targets is designed. Additionally, the convolutional block attention module (CBAM) is incorporated to enhance the feature extraction ability of the neural network for ice-shaped boundaries. Comparative experiments are conducted to validate the performance of the proposed method against other rotated object detection approaches and the baseline network. The results demonstrate that our RA-CenterNet method has a significant competitive advantage over the mainstream rotation-based object detection algorithms.
- Subjects
AIRPLANE windows &; windshields; AERODYNAMICS; FEATURE extraction; DEEP learning; CALIBRATION
- Publication
Transactions of Nanjing University of Aeronautics & Astronautics, 2023, Vol 40, Issue 6, p703
- ISSN
1005-1120
- Publication type
Article
- DOI
10.16356/j.1005‑1120.2023.06.007