Annotation Free Spacecraft Detection and Segmentation using Vision Language Models

2026-02-04

AI Summary

This study addresses the challenge of detecting and segmenting space objects—such as spacecraft—under unlabeled conditions, where low visibility, varying illumination, and background blending severely hinder performance. To tackle this problem, the work introduces visual language models (VLMs) into the domain for the first time, proposing a lightweight training paradigm that leverages VLMs to automatically generate pseudo-labels and integrates them within a teacher–student knowledge distillation framework. The approach eliminates the need for manual annotations while significantly enhancing zero-shot segmentation performance. Experimental results on the SPARK-2024, SPEED+, and TANGO datasets demonstrate consistent improvements, with gains in average precision (AP) of up to 10 percentage points over existing baselines.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Vision Language Models (VLMs) have demonstrated remarkable performance in open-world zero-shot visual recognition. However, their potential in space-related applications remains largely unexplored. In the space domain, accurate manual annotation is particularly challenging due to factors such as low visibility, illumination variations, and object blending with planetary backgrounds. Developing methods that can detect and segment spacecraft and orbital targets without requiring extensive manual labeling is therefore of critical importance. In this work, we propose an annotation-free detection and segmentation pipeline for space targets using VLMs. Our approach begins by automatically generating pseudo-labels for a small subset of unlabeled real data with a pre-trained VLM. These pseudo-labels are then leveraged in a teacher-student label distillation framework to train lightweight models. Despite the inherent noise in the pseudo-labels, the distillation process leads to substantial performance gains over direct zero-shot VLM inference. Experimental evaluations on the SPARK-2024, SPEED+, and TANGO datasets on segmentation tasks demonstrate consistent improvements in average precision (AP) by up to 10 points. Code and models are available at https://github.com/giddyyupp/annotation-free-spacecraft-segmentation.

Problem

• spacecraft detection
• annotation-free
• vision language models
• space segmentation
• zero-shot learning

Innovation

• Vision Language Models
• Annotation-free
• Pseudo-labeling
• Label Distillation
• Spacecraft Segmentation