2026-10-10T16:21:37Z https://www.scidb.cn/oai

10.57760/sciencedb.40181 2026-08-10T18:14:45Z

2026-08-10 Multimodal Dataset of Space Debris doi:10.57760/sciencedb.40181 en # 1. 3D Dataset To construct an authentic and reliable 3D dataset of space debris, hypervelocity impact experiments were reproduced, and a ground experimental system centered on a two-stage light gas gun was established. Collision conditions were simulated inside a vacuum chamber, where projectiles were accelerated to 3–8 km/s. Aluminum alloy plates were used to simulate protective structures, small aluminum beads served as projectiles, and core spacecraft components were selected as impacted targets. Collision scenarios between space debris and spacecraft under multiple working conditions were simulated to generate various debris samples. A total of six groups of hypervelocity impact experiments were carried out, as listed below: (1) Aluminum plate vs. momentum wheel (aluminum plate thickness: 3 mm, projectile mass: 0.325 g, impact velocity: 4.17 km/s); (2) Aluminum plate vs. battery pack (aluminum plate thickness: 5 mm, projectile mass: 0.325 g, impact velocity: 4.09 km/s); (3) Aluminum plate vs. camera lens (aluminum plate thickness: 5 mm, projectile mass: 0.325 g, impact velocity: 4.19 km/s); (4) Solar panel (projectile mass: 0.325 g, impact velocity: 5.89 km/s); (5) Circuit board (projectile mass: 0.7579 g, impact velocity: 5.43 km/s); (6) Camera lens (projectile mass: 0.325 g, impact velocity: 5.89 km/s). Subsequently, the 3D database of space debris was built combined with 3D point cloud reconstruction technology. First, a 3D scanner was adopted to acquire submillimeter-level morphology and feature data. Next, 3D models were reconstructed via point cloud fusion and mesh generation techniques, followed by optimization processes including outlier and overlap removal, surface smoothing, mesh simplification and hole repair. Precise 3D models were finally obtained to form the complete 3D dataset.# 2. Visible Light Dataset PLY models were imported into Blender. An image acquisition method was applied to build a multi-angle visible light dataset by adjusting the rotation angle of models.# 3. Infrared Dataset Taking the DR-AVIT model as the core reference and the DroneVehicle aerial visible-infrared dataset as the foundation, cross-modal image translation between visible light and infrared spectra was realized. High-quality infrared datasets of space debris with diverse radiation characteristics and background interferences were thereby generated.# 4. SAR Dataset Cycle-Consistent Generative Adversarial Network (CycleGAN) was adopted to implement cross-domain translation between visible light and Synthetic Aperture Radar (SAR), converting visible light images into SAR images.# 5. Dataset Scale The dataset adopted in this paper covers three modalities: visible light, infrared and SAR. Each modality contains 29 categories of space debris, namely momentum wheel, momentum wheel fragments, Satellite 1, Satellite 2, unassembled circuit board, Battery 1, Battery 2, battery slot, fragmented momentum wheel, fragmented solar panel, fragmented Battery 1, fragmented Battery 2, fragmented circuit board, fragmented lens, white lens, Lens Fragments 1–12, Black Lens 1 and Black Lens 2.One image was captured every 15° of rotation for each 3D model, producing 24 × 24 = 576 original images per category. Data augmentation was performed in this paper by capturing one image every 30° of rotation, which yielded 576 × 12 = 6912 augmented images for each category. With 29 categories in total, each modality contained 6912 × 29 = 200448 augmented images. To reduce upload volume, only original images (576 images per category) are uploaded; users may perform self-defined data augmentation as required.# 6. Project Support This work was supported by the National Science and Technology Major Project (2022ZD0117301), the National Key Basic Research Project (2022JCJQZD20600), the Key Support For Top Innovation Team Projects (No. 2022-901), the Hundred Talents Program for Scientists (Project No. 2022-500), the National Excellent Professional and Technical Talents (Under 55, Project No. 2022-641). This dataset is used to supplement the publication of the paper "A Space Debris Recognition Method Based on Multimodal Feature Fusion" (DOI: 10.3724/ati2026023). hypervelocity impact experiment; 3D dataset; visible light dataset; infrared dataset; SAR dataset Qiming Cao Jiang Yu Chen Yongle Guo Xiaotao Fan Yu Feng Zeyun Wang Zhelung Wu Qiang Zhang Pinliang PUBLIC https://creativecommons.org/licenses/by-nc-sa/4.0/ dataset http://www.doi.org/10.3724/ati2026023 Science Data Bank