Towards Sub-Millimetre Crack Width Classification in Civil Structures

Fri, 01 Mar 2024 00:00:00 +0000

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

These are client-defined, practitioner classes; no standard is cited for them yet.

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 — smartphone images are also targeted, which would make close-range inspection possible with everyday equipment; early informal trials, not yet quantified.

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.

Objectives & status

Objective: non-destructive, repeatable inspection of civil structures.
The pipeline is designed to produce geolocated defect maps to support maintenance prioritisation; models, validation and visuals are pending.

No accuracy or width-estimation error has been measured yet; field-mission deliverables pending.

Stack

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

Deep Learning on Boris Dupoux