DOI: 10.25835/bveo4v4l
title: Concrete Aggregate PSD Imaging Dataset

description: ## 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.

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## 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 (&gt;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.

![Sensor setup used for data acquisition](https://data.uni-hannover.de/dataset/6f844e22-12ed-48a7-9ccb-b502f8121650/resource/adcf7049-3ad3-4e9c-bd8b-1be00d867f46/download/sensorsetup.jpg " ")

 
 

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## Datasets

Two datasets were created to cover common aggregate size ranges used in concrete production:

### 𝑀ᶠⁱⁿᵉ — Fine Material (&lt; 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.

&gt; **Note:** 
&gt; 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.
&gt; The dataset represents *controlled variability*. While this is ideal for benchmarking and model development, real industrial plants may exhibit additional stochastic variability.

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## 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**.


![Example images and grading curves of the data sets](https://data.uni-hannover.de/dataset/6f844e22-12ed-48a7-9ccb-b502f8121650/resource/2d5cae38-8ad7-4f82-9202-6ac807051c17/download/overview.png " ")

 
 
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## 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

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## 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.*


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