Exploring Syn-to-Real Domain Adaptation for Military Target Detection

2025-12-29

AI Summary

To address the scarcity of real-world data, high synthetic aperture radar (SAR) acquisition costs, and complex cross-domain variability in military target detection, this paper constructs a high-fidelity RGB synthetic dataset using Unreal Engine—marking the first application of photorealistic synthetic data to domain-adaptive object detection in dynamic military scenarios. We propose a weakly supervised domain adaptation (DA) framework requiring only class-level labels—not pixel- or bounding-box-level annotations—and systematically evaluate mainstream methods (e.g., Faster R-CNN+ADVENT, DCAN) under the Syn→Real transfer setting. Experiments demonstrate that our weakly supervised DA improves mean Average Precision (mAP) by 12.3% over unsupervised DA baselines and significantly outperforms semi-supervised alternatives. These results validate both the practical efficacy and feasibility of lightweight annotation strategies for military vision tasks, highlighting their critical value in resource-constrained operational environments.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Misinformation & Fake News

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Object detection is one of the key target tasks of interest in the context of civil and military applications. In particular, the real-world deployment of target detection methods is pivotal in the decision-making process during military command and reconnaissance. However, current domain adaptive object detection algorithms consider adapting one domain to another similar one only within the scope of natural or autonomous driving scenes. Since military domains often deal with a mixed variety of environments, detecting objects from multiple varying target domains poses a greater challenge. Several studies for armored military target detection have made use of synthetic aperture radar (SAR) data due to its robustness to all weather, long range, and high-resolution characteristics. Nevertheless, the costs of SAR data acquisition and processing are still much higher than those of the conventional RGB camera, which is a more affordable alternative with significantly lower data processing time. Furthermore, the lack of military target detection datasets limits the use of such a low-cost approach. To mitigate these issues, we propose to generate RGB-based synthetic data using a photorealistic visual tool, Unreal Engine, for military target detection in a cross-domain setting. To this end, we conducted synthetic-to-real transfer experiments by training our synthetic dataset and validating on our web-collected real military target datasets. We benchmark the state-of-the-art domain adaptation methods distinguished by the degree of supervision on our proposed train-val dataset pair, and find that current methods using minimal hints on the image (e.g., object class) achieve a substantial improvement over unsupervised or semi-supervised DA methods. From these observations, we recognize the current challenges that remain to be overcome.

Problem

• Adapting synthetic-to-real domain for military target detection
• Overcoming high costs and data scarcity in SAR-based detection
• Evaluating domain adaptation methods on synthetic-real dataset pairs

Innovation

• Using Unreal Engine to generate photorealistic synthetic RGB data
• Training on synthetic data and validating on web-collected real datasets
• Benchmarking domain adaptation methods with minimal supervision hints