CERBERUS: Crack Evaluation & Recognition Benchmark for Engineering Reliability & Urban Stability
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 14 references that are in the map. First posted on arXiv 27 Jun 2025.
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2 of 14 references.
References · 2
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2024Gaze-based Human-Robot Interaction System for Infrastructure Inspectionscited by 2
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