CERBERUS：用于工程可靠性与城市稳定性的裂缝评估与识别基准
CERBERUS: Crack Evaluation & Recognition Benchmark for Engineering Reliability & Urban Stability
AI总结：
本文提出合成基准CERBERUS，通过图像生成器和Unity 3D场景训练评估AI裂缝检测模型，实验证明结合合成与真实数据能提升YOLO在真实图像上的检测性能，支持自动化基础设施检查研究。
摘要：
CERBERUS是一个合成基准，旨在帮助训练和评估AI模型以检测基础设施中的裂缝和其他缺陷。它包含一个裂缝图像生成器以及在Unity中构建的真实3D检查场景。该基准包含两种设置：简单的Fly-By墙体检查和具有光照与几何挑战的更复杂的Underpass场景。我们使用合成和真实裂缝数据的不同组合测试了流行的目标检测模型（YOLO）。结果表明，结合合成与真实数据能提升在真实世界图像上的性能。CERBERUS提供了一种灵活、可重复的方法来测试缺陷检测系统，并支持未来在自动化基础设施检查方面的研究。CERBERUS公开于https://github.com/justinreinman/Cerberus-Defect-Generator。
英文摘要：
CERBERUS is a synthetic benchmark designed to help train and evaluate AI models for detecting cracks and other defects in infrastructure. It includes a crack image generator and realistic 3D inspection scenarios built in Unity. The benchmark features two types of setups: a simple Fly-By wall inspection and a more complex Underpass scene with lighting and geometry challenges. We tested a popular object detection model (YOLO) using different combinations of synthetic and real crack data. Results show that combining synthetic and real data improves performance on real-world images. CERBERUS provides a flexible, repeatable way to test defect detection systems and supports future research in automated infrastructure inspection. CERBERUS is publicly available at https://github.com/justinreinman/Cerberus-Defect-Generator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
