# CERBERUS: Crack Evaluation & Recognition Benchmark for Engineering Reliability & Urban Stability

arXiv: https://arxiv.org/abs/2506.21909 · HTML: https://mlsanity.com/paper/2506.21909/

## 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.

## Builds on (most cited first, 2 of 2)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Gaze-based Human-Robot Interaction System for Infrastructure Inspections](https://mlsanity.com/paper/2403.08061/) (2024)

## 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.