Transactions of Nanjing University of Aeronautics & Astronautics
An Improved CenterNet Method for Wing Icing Detection
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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.
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Project Supported:
This work was supported by the Key Laboratory of Icing and Anti/De-icing of China Aerodynamics Research and Development Center（CARDC） （No.IADL20210203）， the Natural Science Foundation of Sichuan， China （No.2023NSFSC1393）， the Scientific Research Starting Project of Southwest Petroleum University （SWPU） （No.2021QHZ001）， and the National Natural Science Foundation of China （No.52006235）. All data in this paper are supported by the Key Laboratory of Icing and Anti/De-icing of CARDC. Besides， the authors would like to acknowledge the following people for their assistance： ZHANG Quan， YANG Xinling， WANG Tianfei and WANG Shun.
Clc Number:
TP391
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