AI 中文总结
本研究利用CycleGAN-Turbo生成合成热图像并扩展真实热数据，训练多种检测器，证明合成数据可减少对红外数据集的依赖，实现低可见度下实时动物检测，RT-DETR和YOLOv10s分别取得最佳合成与增强数据性能。
AI 中文摘要
野生动物与车辆碰撞仍然是道路安全的重要问题，尤其是在夜间和低可见度条件下，基于RGB的感知系统往往不可靠。热成像为在光照不足条件下检测动物提供了一种有前景的替代方案。然而，标注的红外动物数据集的有限可用性限制了基于深度学习的鲁棒检测模型的发展。本文研究了合成热图像生成作为一种可扩展的方法，用于低可见度条件下的实时动物检测。从NTLNP数据集中选取514张标注的可见光谱动物图像子集，使用CycleGAN-Turbo将其转换为合成热表示，同时通过热聚焦增强扩展了60张有限真实热数据集。多种目标检测架构，包括YOLOv8、YOLOv9、YOLOv10和RT-DETR，分别在合成和真实热数据集上独立训练，并使用精确率、召回率、mAP@0.5、mAP@0.5:0.95、模型大小和推理延迟进行评估。实验结果表明，合成热图像提供了具有竞争力的检测性能，其中RT-DETR在合成数据上取得了最高的mAP@0.5，达到0.9613。在增强的真实热数据上训练的模型取得了最强的整体性能，YOLOv10s获得了0.9879的mAP@0.5和0.9571的mAP@0.5:0.95。计算分析进一步表明，轻量级YOLO变体提供了有利的推理延迟，支持其实时部署的潜力。这些发现表明，合成热图像可以减少对稀缺红外数据集的依赖，并支持开发高效的动物检测系统，以用于未来车载野生动物碰撞缓解应用。
英文摘要
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, mAP@0.5, mAP@0.5:0.95, model size, and inference latency. Experimental results show that synthetic thermal images provide competitive detection performance, with RT-DETR achieving the highest synthetic-data mAP@0.5 of 0.9613. Models trained on augmented real thermal data achieve the strongest overall performance, with YOLOv10s obtaining 0.9879 mAP@0.5 and 0.9571 mAP@0.5:0.95. 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.
CommentsAccepted at The IEEE Cyber Awareness Research Symposium (CARS), 2026
