360° LIDAR‑Based Crack Detection and Characterisation for Infrastructure Inspection

| Limitation | Conventional Method | 360° LIDAR Approach | |------------|--------------------|---------------------| | | Limited to line‑of‑sight, often requires multiple passes | Whole‑scene capture in a single scan | | Subjectivity | Dependent on inspector experience | Objective geometric measurements | | Speed | Hours to days for large structures | Minutes per scan (≤ 5 min) | | Environmental constraints | Poor lighting, weather sensitivity | Independent of illumination; works in low‑light | | Data richness | 2‑D images only | 3‑D geometry + intensity + RGB (when fused) |

A. Smith¹, B. Lee², C. Martínez³, D. Khan⁴

¹ Department of Civil Engineering, University of X ² Center for Robotics and Perception, Institute Y ³ Department of Computer Science, University Z ⁴ National Laboratory for Infrastructure Safety

A. Smith (asmith@univx.edu) Abstract Crack formation is a primary indicator of structural deterioration in concrete, asphalt, and rock surfaces. Conventional visual inspection is labor‑intensive, subjective, and limited to line‑of‑sight. This paper presents a fully automated 360° LIDAR‑based crack detection (LIDAR‑360‑Crack) pipeline that exploits high‑resolution terrestrial laser scanning (TLS) to acquire dense point clouds of entire structural façades, bridges, tunnels and pavements in a single sweep. By integrating multi‑scale geometric descriptors, intensity‑based filtering, and a lightweight deep‑learning classifier, the system extracts crack geometries, quantifies their width, depth and orientation, and generates GIS‑compatible vector maps. Extensive field trials on three bridge decks, two highway sections and a historic stone wall demonstrate detection accuracies of 94.2 % (precision) / 91.8 % (recall) , with mean absolute width error < 0.4 mm. The proposed framework reduces on‑site inspection time by 70 % relative to manual methods and offers a reproducible dataset for long‑term structural health monitoring.