摘要
Abstract
Spacecraft operating in complex space environments are susceptible to micro-scale surface damage,which poses threats to structural integrity and mission reliability.To overcome the limitations in scale modeling,feature alignment,and environmental adaptability of existing detection techniques,a novel spacecraft surface micro-damage detection method based on a dual-branch multi-scale Vision Transformer(ViT)is proposed.In the proposed architecture,global environmental context and local high-resolution detail features are extracted,and a saliency-guided fusion module(SGFM)is employed to dynamically aggregate critical regions and enhance the detection accuracy of minute damages.The method was evaluated on the Spacecraft-DS dataset for impurity and scratch detection on metallic housings within an end-to-end training framework.Compared with mainstream models such as YOLO-V8 and DETR,the proposed approach achieved improvements of up to 2.2%in APall,Recall,and F1,and reached an inference speed of 25 FPS.Superior performance was observed particularly in small-sized damage recognition and complex-background suppression,effectively addressing the limited receptive field of traditional CNNs and the scale-adaptability issues of standard ViTs.The results demonstrate that the proposed method provides an efficient technical solution for applications such as space-station inspection and satellite degradation analysis.关键词
航天器表面微损伤/智能检测/双分支多尺度ViT/语义门控特征融合/小目标检测/航天器健康管理Key words
spacecraft surface micro-damage/intelligent detection/dual-branch multi-scale vision transformer(ViT)/semantic-gated feature fusion/small-target detection/spacecraft health management分类
航空航天
