Towards Autonomous Aircraft Surveillance from Nanosatellites through On-Board Inference and Generative Data Augmentation
Authors: Antonio Delgado-Rosa, David Muñoz-Valero, Enrique Adrian Villarrubia-Martin, Juan Moreno-Garcia
Organizations: Escuela de Ingeniería Industrial y Aeroespacial de Toledo, Department of Technologies and Information Systems, Universidad de Castilla–La Mancha, Avenida Carlos III, s/n, Toledo, 45071, Spain · Escuela Superior de Informática, Department of Technologies and Information Systems, Universidad de Castilla-La Mancha, Paseo de la Universidad 4, Ciudad Real, 13071, Spain
Abstract
Airborne surveillance from low Earth orbit is hindered by two interconnected bottlenecks: nanosatellites have a limited downlink budget, yet the conventional approach still transmits terabytes of raw imagery to the ground for processing, and open satellite datasets for aircraft are scarce and severely class-imbalanced. These limitations either delay timely decision-making or prevent standard detectors from learning robust representations of rare aircraft classes. In this paper, a workflow that combines on-board inference with generative data augmentation is proposed to address both limitations jointly. Inference is executed on a 6U CubeSat equipped with a low-power edge tensor accelerator, while a diffusion model fine-tuned through low-rank adaptation generates synthetic minority-class imagery. This synthetic output is automatically annotated, pseudo-labelled, by an intermediate detector and merged with classically augmented samples. The results show that the balanced dataset increases global mean average precision from 77.9% to 82.2%, with the minority class rising from F1=0.683 to F1=0.811, and that the quantised detector fits the on-chip memory and projects 25-30 frames per second on orbit. This approach contrasts with the conventional bent-pipe architecture, in which the satellite acts as a passive data collector. Therefore, the computational tests support the proposed workflow as a decision-support tool for real-time, autonomous airborne surveillance from nanosatellites.
Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes. Beyond data augmentation, their potential as diagnostic tools for trained vision systems remains unexplored in the aerial and remote sensing domains. We introduce a synthetic diagnostic framework for aerial-view vehicle detection that combines text-guided generation, attribute-controlled editing, and automated attribute verification to construct a controllable synthetic testbed. This enables fine-grained evaluation of pretrained detectors under diverse scene types and environmental conditions that are difficult to isolate in real datasets. Across three detection architectures and three real aerial datasets, synthetic scene-wise performance trends closely match real-world weaknesses. Guided by these diagnostics, targeted supplementation with small real datasets from the identified weak categories yields improvements of up to 13% AP50 while requiring substantially fewer additional samples than non-targeted augmentation. Our results show that controlled synthetic probing can predict real-domain performance gaps and guide efficient data collection. The proposed diagnostic framework is modular and can incorporate alternative generative or vision-language models as capabilities evolve. Our code and datasets are available here: https://humansensinglab.github.io/AVODDiag/
Stanislav Panev, Minhyek Jeon, Vaishnavi Khindkar +5
Visual detection of Unmanned Aerial Vehicles (UAVs) is a critical task in surveillance systems due to their small physical size and environmental challenges. Although deep learning models have achieved significant progress, deploying them on edge devices necessitates the use of lightweight models, such as YOLOv11 Nano, which possess limited learning capacity. In this research, an efficient and context-aware data augmentation pipeline, combining Mosaic strategies and HSV color-space adaptation, is proposed to enhance the performance of these models. Experimental results on four standard datasets demonstrate that the proposed approach, compared to heavy and instance-level methods like Copy-Paste, not only prevents the generation of synthetic artifacts and overfitting but also significantly improves mean Average Precision (mAP) across all scenarios. Furthermore, the evaluation of generalization capability under foggy conditions revealed that the proposed method offers the optimal balance between Precision and stability for real-time systems, whereas alternative methods, such as MixUp, are effective only in specific applications.
The object detection in satellite imagery has garnered considerable attention due to its extensive real-world applications and the inherent challenges it presents, including noise, fluctuating image quality, and intricate backgrounds. This paper proposed a framework for object detection that combines image enhancement and Deep Learning (DL) to make detection more accurate. First, a Gabor filter is used to process the input image to bring out important features and reduce noise. Then, normalization is applied to make sure that the data is evenly distributed so that the model works properly. After that, a model based on YOLOv11 is used to quickly learn and find object features. The proposed method achieves a mAP of 95%, precision of 97%, recall of 85%, and F1-score of 91%, which demonstrates the superior aircraft detection performance. These results show the framework accurately identify aircraft in satellite imagery and is suitable for real-time applications such as surveillance, air traffic monitoring and remote sensing analysis.
Mujahir Hussain Abbasi, S. Beghin, A. T. R. Krishna Priya +3