Code and data for segmentation of granular material images using synthetic data and deep learning

Description

This SyntheticSoil project is a Unity-based pipeline designed to procedurally generate and annotate realistic granular soil images from granulometric data. It enables the simulation of soil samples based on real-world particle size distributions (PSDs), their segmentation via Unity Perception, and their conversion to standard annotation formats (COCO, Pascal VOC) for training deep learning models such as Mask R-CNN. --- ## 📁 Repository Structure ``` SyntheticSoilProject/ │ ├── 0_AnnotationSyntheticSoil/ # Unity project to generate and annotate soil images (top + bottom views) — version A (original project used in the article) ├── 1_solo2coco/ # Converts Unity SOLO annotations to COCO format using pysolotools ├── 2_polygon_vers_rle/ # Converts polygon annotations (x,y) to COCO RLE format ├── 3_json2xml/ # Converts COCO JSON annotations to Pascal VOC (XML) ├── 4_GrainSegNet/ # GrainSegNet model (training & inference code, checkpoints, evaluation scripts) ├── 5_SyntheticSoil/ # Unity project to generate high-resolution images without annotation — *version B (alternate codebase used in article) ├── 6_Annexes/ # Additional examples, outputs and test files │ ├── 0_AnnotationSyntheticSoil/ # Example scene's images │ ├── 1_Example_of_solo_data/ # Example outputs from the annotation Unity project (images, masks, SOLO JSON) │ ├── 2_Example_of_SyntheticSoil_Images/ Example RGB images and instance masks │ ├── 3_Test_GrainSegNet/ Example test outputs and evaluation of GrainSegNet │ ├── Real_granular_material.JPG Example real sample photograph │ └── Some_granulo_desired.txt Example PSD text file with desired granulometry ├── 7_BuildSyntheticSoil/ Builder version containing the full set of particle models used by the author (pre-processed prefabs) └── README.md ```

Institutions

• Ecole de technologie superieure

Categories

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