Aerial & Drone

Sun, 01 Jun 2025 00:00:00 +0000

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Pollutant Detection by Drone (Day & Night)

Mon, 01 Jul 2024 00:00:00 +0000

Context

Detect gas and hydrocarbon pollutants from a drone, day and night, to monitor mobile pollution sources (vessels, machinery) in maritime and industrial zones.

Pipeline

flowchart TB
 A["Drone — thermal/IR + visible"] --> B["Day / night acquisition"]
 B --> C["Pollutant signature detection"]
 C --> D["Localisation on moving targets"]
 D --> E["Maritime / industrial monitoring"]

Methods

Combined thermal/infrared and visible-spectrum imaging.
Detection and localisation of pollutant traces on moving targets.
Operational in both daytime and nighttime conditions.

Results

Detection of fuel leaks and hydrocarbon discharge in port areas and coastal waters.
Continuous monitoring capability for maritime and industrial zones.

Stack

Python · OpenCV · thermal/IR imaging · drone

Sub-Millimetre Crack Detection 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

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

Drone on Boris Dupoux

Aerial & Drone

Pollutant Detection by Drone (Day & Night)

Context

Pipeline

Methods

Results

Stack

Sub-Millimetre Crack Detection in Civil Structures

Context

Four width classes

The sub-pixel challenge

Acquisition: drone and smartphone

Pipeline

Methods

Results

Stack