Concrete Aggregate PSD Imaging Dataset

## Dataset Summary
This repository contains the data related to the paper

**"Coenen, M., Beyer, D., Mohammadi, S., Meyer, M., Heipke, C., and Haist, M. (2026): Automating Concrete Production Control with Computer Vision-based Aggregate Characterisation. In: Automation in Construction."**

This dataset provides image data of concrete aggregate for the task of **estimating particle size distributions (PSD)** using computer vision. The images were captured using a controlled camera setup installed above a conveyor belt in a concrete mixing research facility. Each image has a corresponding text file containing the ground-truth PSD obtained through mechanical sieving.

---

## Data Acquisition
Data was recorded at a medium-scale concrete mixing plant equipped with:

- **Two Allied Vision Alvium 1800 C-508 cameras**
- 25 mm focal length → fine aggregate (0–2 mm)
- 12 mm focal length → coarse aggregate (>2 mm)
- **Global shutter**, 1 ms exposure time
- **LED panel illumination** for motion-blur-free imaging
- A **sensor mount above the conveyor belt** transporting the aggregate

This setup enabled consistent imaging conditions with sufficient resolution for particle analysis.


---

## Datasets

Two datasets were created to cover common aggregate size ranges used in concrete production:

### 𝑀ᶠⁱⁿᵉ — Fine Material (< 2 mm)
- 16 material samples
- Natural river sand
- PSDs synthetically varied by mixing pre-fractionated material

### 𝑀ᶜᵒᵃʳˢᵉ — Coarse Material (2–16 mm)
- 26 material samples
- 16 natural river gravel
- 10 recycled concrete aggregate (RCA)

Each material sample weighs **150 kg**, and its PSD was systematically varied to cover a broad range of grading curves.

> **Note:**
> This repository contains only a subset of the data set that was used in the paper. In order to receive the full data set, please reach out to the authors.
> The dataset represents *controlled variability*. While this is ideal for benchmarking and model development, real industrial plants may exhibit additional stochastic variability.

---

## Reference PSD Measurement

A **10 kg subsample** from each material batch was mechanically sieved to obtain the reference PSD.

Each PSD is represented using **six particle size intervals (B = 6):**

- **Fine dataset:**
`0.063, 0.125, 0.25, 0.5, 1.0, 2.0 mm`
- **Coarse dataset:**
`0, 2, 4, 8, 11.2, 16 mm`

Each `.txt` reference file contains six percentile values that sum to **1.0**.


---

## Use Cases

This dataset is intended for:

- PSD estimation using deep learning or classical CV
- Regression and distribution prediction tasks
- Material characterization and granulometry research
- Benchmarking computer vision methods on granular material datasets

---

## Citation

If you use this dataset in academic or industrial research, please cite the corresponding paper:

*Coenen, M., Beyer, D., Mohammadi, S., Meyer, M., Heipke, C., and Haist, M. (2026): Automating Concrete Production Control with Computer Vision-based Aggregate Characterisation. In: Automation in Construction.*

---