Class-specific diffusion models improve military object detection in a low-data domain

TL;DR

This paper demonstrates that class-specific diffusion models can significantly enhance military vehicle detection accuracy in low-data scenarios, serving as an effective alternative to traditional data collection methods.

Contribution

It introduces a method to fine-tune diffusion models for class-specific military object generation and shows their effectiveness in improving detection performance with minimal real data.

Findings

Diffusion-generated images improved detection performance up to +8.0% mAP$_{50}$ with 8 real samples.

Structural guidance with ControlNet further enhanced performance in low-data regimes.

Synthetic data can replace traditional simulation pipelines for military AI training.

Abstract

Diffusion-based image synthesis has emerged as a promising source of synthetic training data for AI-based object detection and classification. In this work, we investigate whether images generated with diffusion can improve military vehicle detection under low-data conditions. We fine-tuned the text-to-image diffusion model FLUX.1 [dev] using LoRA with only 8 or 24 real images per class across 15 vehicle categories, resulting in class-specific diffusion models, which were used to generate new samples from automatically generated text prompts. The same real images were used to fine-tune the RF-DETR detector for a 15-class object detection task. Synthetic datasets generated by the diffusion models were then used to further improve detector performance. Importantly, no additional real data was required, as the generative models leveraged the same limited training samples. FLUX-generated images improved detection performance, particularly in the low-data regime (up to +8.0% mAP$_{50}$ with 8 real samples). To address the limited geometric control of text prompt-based diffusion, we additionally generated structurally guided synthetic data using ControlNet with Canny edge-map conditioning, yielding a FLUX-ControlNet (FLUX-CN) dataset with explicit control over viewpoint and pose. Structural guidance further enhanced performance when data is scarce (+4.1% mAP$_{50}$ with 8 real samples), but no additional benefit was observed when more real data is available. This study demonstrates that object-specific diffusion models are effective for improving military object detection in a low-data domain, and that structural guidance is most beneficial when real data is highly limited. These results highlight generative image data as an alternative to traditional simulation pipelines for the training of military AI systems.