Wildlife-vehicle collisions remain a significant road safety concern, particularly during nighttime and low-visibility conditions when RGB-based perception systems are often unreliable. Thermal imaging offers a promising alternative for detecting animals under poor illumination. However, the limited availability of annotated infrared animal datasets restricts the development of robust deep learning-based detection models. This paper investigates synthetic thermal image generation as a scalable approach for real-time animal detection under low-visibility conditions. A subset of 514 annotated visible-spectrum animal images from the NTLNP dataset is translated into synthetic thermal representations using CycleGAN-Turbo, while a limited real thermal dataset of 60 images is expanded through thermal-focused augmentation. Multiple object detection architectures, including YOLOv8, YOLOv9, YOLOv10, and RT-DETR, are trained independently on synthetic and real thermal datasets and evaluated using precision, recall, [email protected], [email protected]:0.95, model size, and inference latency. Experimental results show that synthetic thermal images provide competitive detection performance, with RT-DETR achieving the highest synthetic-data [email protected] of 0.9613. Models trained on augmented real thermal data achieve the strongest overall performance, with YOLOv10s obtaining 0.9879 [email protected] and 0.9571 [email protected]:0.95. Computational analysis further indicates that lightweight YOLO variants provide favorable inference latency, supporting their potential for real-time deployment. These findings demonstrate that synthetic thermal imagery can reduce dependence on scarce infrared datasets and support the development of efficient animal detection systems for future vehicle-mounted wildlife collision mitigation applications.
Figures & tables
Fig. 1 : Proposed thermal animal detection framework. RGB wildlife images are translated into synthetic thermal representations using CycleGAN-Turbo. Object detection models are trained independently on synthetic and augmented real thermal datasets and evaluated for detection accuracy and computational efficiency.
Fig. 3 : Examples of synthetic and real thermal animal images. (a) Synthetic thermal images generated from RGB images; (b) Real thermal images collected from publicly available sources.
Model
Dataset
mAP0.5
mAP0.5:0.95
Precision
Recall
YOLOv8n
Synthetic
0.9318
0.6788
0.7857
0.9662
YOLOv8n
Real Thermal
0.9827
0.9493
0.8384
0.9140
YOLOv8s
Synthetic
0.8837
0.6821
0.8922
0.8263
YOLOv8s
Real Thermal
0.9874
0.9527
0.9374
0.9120
YOLOv9t
Synthetic
0.8683
0.6596
0.9070
0.8658
YOLOv9t
Real Thermal
0.9831
0.9483
0.8890
0.8876
TABLE I : Detection performance comparison of object detection models across synthetic and real thermal datasets.
Model
Parameters (M)
Latency (ms)
GFLOPs
YOLOv8n
3.0
8.37–14.07
8.1
YOLOv8s
11.1
8.58–14.25
28.4
YOLOv9t
2.0
14.44–20.25
7.6
YOLOv9s
7.2
15.23–20.87
26.7
YOLOv10n
2.3
8.91–14.78
6.5
YOLOv10s
7.2
9.11–14.95
21.4
TABLE II : Computational efficiency comparison of the detection models.
Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information, sensor noise, weak thermal contrast, and the scarcity of annotated data. This work investigates a synthetic-first training strategy that combines synthetic scene generation with fine-tuning on real data. We show that synthetic data provides an effective basis for learning initial object representations, while real in-domain thermal imagery is still essential for reliable deployment. Even small amounts of real IR data substantially reduce domain gaps. Our experiments indicate that dataset alignment has a stronger impact on performance than model scale. Finally, our analysis of the dataset suggests that semantic alignment in feature space is the strongest predictor of model performance, while radiometric properties such as entropy and dynamic range also contribute to detection robustness. This work provides a foundation for combining synthetic and real IR data for effective G2A drone detection.
Tanel Liiv, Sander Soodla, Nzamba Bignoumba +2
Marduk Technologies OÜ, Tallinn, Estonia · Intelligent Imaging, TNO Defence, Safety and Security, The Hague, The Netherlands
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% mAP50 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% mAP50 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.
Ella P. Fokkinga, Jan Erik van Woerden, Thijs A. Eker +4
TNO - Intelligent Imaging, Oude Waalsdorperweg 63, the Hague, the Netherlands
Existing open-vocabulary detectors focus on RGB images and fail to generalize to thermal imagery, where low texture and emissivity variations challenge RGB-based semantics. We present Thermal-Det, the first large language model (LLM) supervised open-vocabulary detector tailored for thermal images. To enable large-scale training, we develop a synthetic dataset by converting GroundingCap-1M into the thermal domain and filtering captions to remove RGB-specific terms, yielding over one million thermally aligned samples with bounding boxes, grounding texts, and detailed captions. Thermal-Det jointly optimizes detection, captioning, and cross-modal distillation objectives. A frozen RGB teacher provides geometric and semantic pseudo-supervision for paired but unlabeled RGB-thermal data, transferring open-vocabulary knowledge without manual annotation. The model further employs a Thermal-Text Alignment Head for text calibration and a Modality-Fused Cross-Attention module for dual-modality reasoning. Unlike prior domain-adaptation methods, the detector is fully fine-tuned to internalize thermal contrast patterns while preserving language alignment. Experiments on public benchmarks show consistent 2-4% AP gains over existing open-vocabulary detectors, establishing a strong foundation for scalable, language-driven thermal perception.