| | --- |
| | license: cc-by-4.0 |
| | tags: |
| | - computer-vision |
| | - semantic-segmentation |
| | - mining |
| | - industrial-inspection |
| | - monorail |
| | - underground-environment |
| | --- |
| | |
| | # Mine Monorail Track Segmentation Dataset |
| |
|
| | This dataset supports semantic segmentation of **mine-use suspended monorails** in simulated underground environments. It was collected in a dedicated experimental workshop provided by an industrial partner and includes images captured under diverse structural and illumination conditions to reflect realistic operational challenges. |
| |
|
| | ## 📷 Data Collection |
| |
|
| | - **Camera**: Hikvision DS-2CD2820FWD |
| | - **Sensor**: 1/2.8" Progressive Scan CMOS |
| | - **Resolution**: 1280×720 (from native 1920×1080) |
| |
|
| | Images were captured in a controlled workshop that replicates key characteristics of underground monorail roadways, including track geometry, lighting variability, and common obstructions. |
| |
|
| | ## 🗂️ Dataset Statistics |
| |
|
| | - **Total images**: 2,681 |
| | - **Annotation tool**: LabelMe (pixel-level precision) |
| | - **Classes**: |
| | - `0`: Background |
| | - `1`: Monorail Track |
| | - **Track configurations**: |
| | - Straight segments |
| | - Curved sections |
| | - Ascending slopes |
| | - Descending slopes |
| | - **Illumination conditions**: |
| | - Normal lighting |
| | - Low-light |
| | - Overexposed |
| |
|
| | Each image is paired with a single-channel PNG mask where pixel values correspond to class IDs. |
| |
|
| | ## 🔧 Data Augmentation (for reference) |
| |
|
| | To improve model robustness under harsh mining conditions, the authors employed a specialized data augmentation pipeline during training that simulates: |
| | - **Low-light scenarios** through controlled brightness reduction, |
| | - **Sensor noise** via Gaussian and salt-and-pepper noise injection, |
| | - **Dust interference** using color overlays, texture synthesis, and visibility attenuation. |
| |
|
| | > 💡 **Note**: The raw dataset contains only original images and ground-truth masks—**no augmented samples are included**. Full technical details of the augmentation strategy are provided in the associated paper (Section 4.5). |
| |
|
| | ## 📁 Directory Structure |
| |
|
| | ```monorail-seg/ |
| | ├── images/ |
| | │ ├── Training_Input/ |
| | │ ├── Validation_Input/ |
| | │ └── Test_Input/ |
| | ├── labels/ |
| | │ ├── Training_GroundTruth/ |
| | │ ├── Validation_GroundTruth/ |
| | │ └── Test_GroundTruth/ |
| | ├── README.md |
| | └── LICENSE |
| | ``` |
