Sub-Millimetre Crack Detection in Civil Structures

Key figures

width classes, each matched to a repair method: 4
finest width class, often narrower than a pixel: < 0.3 mm
acquisition modes: drone and smartphone: 2

Key notions

sub-pixel detection
crack width classification
structural health monitoring
metric calibration
geolocated defect maps

Abstract

Non-destructive structural health monitoring of bridges, buildings and infrastructure requires not only detecting cracks in concrete and masonry but classifying them by width, since each width class calls for a different repair method. This project addresses crack detection and width classification from drone or smartphone imagery, without contact or scaffolding, into four classes (< 0.3 mm, 0.3–0.7 mm, 0.7–1 mm, ≥ 1 mm). The central challenge is sub-pixel detection: a 0.3 mm crack does not cover a full pixel at typical inspection resolutions. The approach combines computer-vision / deep-learning segmentation with metric calibration and sub-pixel analysis, exploiting the faint partial-pixel contrast that repeats coherently along a continuous, elongated crack. The pipeline yields geolocated defect maps to support maintenance prioritisation; models and visuals are forthcoming.

Context

Non-destructive structural health monitoring for bridges, buildings and infrastructure. The challenge is not only to detect cracks, but to classify them by width — without contact or scaffolding.

Width is what matters for inspection: depending on how open a crack is, the repair (filling) method is not the same. An inspection system therefore has to be very precise, because a wrong width class means a wrong repair specification.

Four width classes

Class	Crack width	Consequence
1	< 0.3 mm	Finest cracks — often narrower than a pixel; dedicated repair approach
2	0.3 – 0.7 mm	Different repair approach
3	0.7 – 1 mm	Different repair approach
4	≥ 1 mm	Different repair approach

Each class calls for its own filling/repair approach, so the classification directly drives the maintenance decision.

The sub-pixel challenge

The key difficulty is to go below the pixel size. A 0.3 mm crack does not even cover a full pixel at typical inspection resolutions — and the system must still detect it, even when the ground sampling distance is somewhat coarser than 0.3 mm.

Why is this possible at all? A crack thinner than a pixel does not vanish: it still occupies part of the pixels it crosses, and slightly alters their intensity compared with the surrounding surface. Taken individually, that partial-pixel signal is weak; but a crack is a continuous, elongated feature, so the same faint contrast repeats coherently along many adjacent pixels. Exploiting that contrast and continuity is what allows detection — and width estimation — beyond the nominal resolution of the image.

Acquisition: drone and smartphone

Drone photography — high-resolution UAV capture of structural surfaces, for large or hard-to-reach structures.
Smartphone photography — the approach also works on images taken with a mobile phone, which I have tested, making close-range inspection possible with everyday equipment.

Pipeline

flowchart TB
    A["Image capture — drone or smartphone"] --> B["Image tiling & calibration"]
    B --> C["Crack segmentation — CV / DL"]
    C --> D["Sub-pixel width estimation"]
    D --> E["Width classification — 4 classes"]
    E --> F["Defect map & inspection report"]

Methods

High-resolution capture of structural surfaces by drone or smartphone.
Computer-vision / deep-learning crack segmentation on concrete and masonry.
Metric calibration and sub-pixel analysis for crack width estimation.
Classification into four width classes matched to repair methods.

Results

Non-destructive, repeatable inspection of civil structures.
Geolocated defect maps supporting maintenance prioritisation.

Models and visuals will be added.

Stack

Python · OpenCV · deep learning · high-res RGB drone · smartphone imagery · photogrammetry