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Lightweight CNN-Based Method for Spacecraft Component Detection

Liu, Yuepeng ; Zhou, Xingyu ; Han, Hongwei

Aerospace, 2022-12, Vol.9 (12), p.761 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Lightweight CNN-Based Method for Spacecraft Component Detection
Autor: Liu, Yuepeng ; Zhou, Xingyu ; Han, Hongwei
Assuntos: Accuracy ; Algorithms ; Analysis ; Artificial neural networks ; Classification ; Data storage ; Datasets ; Feature extraction ; Information storage and retrieval ; Lightweight ; Methods ; Neural networks ; Numerical analysis ; Numerical simulations ; Orbital rendezvous ; Space missions ; Space rendezvous ; Space ships ; Space vehicles ; Spacecraft ; spacecraft component detection intelligent algorithm ; Spacecraft components ; Spacecraft modules
É parte de: Aerospace, 2022-12, Vol.9 (12), p.761
Descrição: Spacecraft component detection is essential for space missions, such as for rendezvous and on-orbit assembly. Traditional intelligent detection algorithms suffer from drawbacks related to high computational burden, and are not applicable for on-board use. This paper proposes a convolutional neural network (CNN)-based lightweight algorithm for spacecraft component detection. A lightweight approach based on the Ghost module and channel compression is first presented to decrease the amount of processing and data storage required by the detection algorithm. To improve feature extraction, we analyze the characteristics of spacecraft imagery, and multi-head self-attention is used. In addition, a weighted bidirectional feature pyramid network is incorporated into the algorithm to increase precision. Numerical simulations show that the proposed method can drastically reduce the computational overhead while still guaranteeing good detection precision.
Editor: Basel: MDPI AG
Idioma: Inglês

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Lightweight CNN-Based Method for Spacecraft Component Detection

Liu, Yuepeng ; Zhou, Xingyu ; Han, Hongwei

Basel: MDPI AG

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