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 Pin Connector Crimp Terminals 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 ESTER-C 500MG ORANGE 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.