| | --- |
| | license: mit |
| | task_categories: |
| | - image-segmentation |
| | tags: |
| | - wood |
| | - defect-detection |
| | - quality-control |
| | - manufacturing |
| | - computer-vision |
| | - semantic-segmentation |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | |
| | # Dataset Card for Oak Defect Detection |
| |
|
| | ## Dataset Description |
| |
|
| | ### Dataset Summary |
| |
|
| | The Oak Defect Detection dataset contains high-resolution line-scan imagery of green rough oak planks with pixel-level annotations for defect detection. The dataset is designed for semantic segmentation tasks to identify and classify defects in wood planks, including BlackRot, Knots, and Stains. |
| |
|
| | This dataset was collected using line-scan imaging systems and includes both color images and corresponding binary masks for each defect class. The data has been processed and validated to ensure image-mask alignment and quality. |
| |
|
| | ### Supported Tasks and Leaderboards |
| |
|
| | - **Semantic Segmentation**: The primary task is pixel-level classification of defects in oak planks |
| | - **Quality Control**: Can be used for automated quality assessment in wood processing |
| | - **Defect Classification**: Multi-class segmentation with classes: Background, BlackRot, Knot, Stain |
| |
|
| | ### Languages |
| |
|
| | N/A (Image dataset) |
| |
|
| | ## Dataset Structure |
| |
|
| | ### Data Instances |
| |
|
| | Each instance consists of: |
| | - **Color Image**: High-resolution TIFF image (`*_Col.tif`) captured from line-scan system |
| | - **Binary Masks**: Per-class binary masks (`*_Col_Bin_<ClassName>.tif`) for each defect type |
| | - **Metadata**: Timestamp and position information encoded in filenames |
| |
|
| | ### Data Fields |
| |
|
| | - **Images**: RGB TIFF format, variable resolution (typical line-scan dimensions) |
| | - **Masks**: Binary TIFF format, same resolution as corresponding images |
| | - **Class Labels**: |
| | - Background (class 0) |
| | - BlackRot (class 1) |
| | - Knot (class 2) |
| | - Stain (class 3) |
| |
|
| | ### Data Splits |
| |
|
| | The dataset includes train/validation/test splits stored in `split.json`. The splits are designed to ensure: |
| | - Representative distribution of defect classes |
| | - No data leakage between splits |
| | - Sufficient samples for training and evaluation |
| |
|
| | ## Dataset Creation |
| |
|
| | ### Curation Rationale |
| |
|
| | This dataset was created to support automated defect detection in wood processing pipelines. The line-scan imagery captures high-resolution details necessary for identifying subtle defects that affect wood quality and value. |
| |
|
| | ### Source Data |
| |
|
| | #### Initial Data Collection and Normalization |
| |
|
| | - **Data Collection**: Images captured using line-scan imaging systems |
| | - **Annotation**: Binary masks created for each defect class |
| | - **Normalization**: Defect class names were normalized (e.g., "Black_Rot" → "BlackRot") to ensure consistency |
| | - **Filtering**: Rare classes below a threshold were filtered to reduce class imbalance |
| | |
| | #### Who are the source language producers? |
| | |
| | Industrial line-scan imaging systems in wood processing facilities. |
| | |
| | ### Annotations |
| | |
| | #### Annotation process |
| | |
| | Binary masks were created for each defect class, with each mask file corresponding to a specific defect type in a specific image. |
| | |
| | #### Who are the annotators? |
| | |
| | Annotations were created as part of the wood processing quality control workflow. |
| | |
| | ### Personal and Sensitive Information |
| | |
| | No personal or sensitive information is present in this dataset. |
| | |
| | ## Considerations for Using the Data |
| | |
| | ### Social Impact of Dataset |
| | |
| | This dataset supports automated quality control in wood processing, which can: |
| | - Improve efficiency in manufacturing |
| | - Reduce waste through early defect detection |
| | - Standardize quality assessment processes |
| | |
| | ### Discussion of Biases |
| | |
| | - **Class Imbalance**: Some defect classes (e.g., Knot) may be more common than others (e.g., Stain) |
| | - **Image Conditions**: All images captured under controlled line-scan conditions |
| | - **Geographic Bias**: Dataset may reflect specific wood sources or processing conditions |
| | |
| | ### Other Known Limitations |
| | |
| | - Limited to green rough oak planks |
| | - Line-scan specific imaging conditions |
| | - Some rare defect classes may have been filtered out during preprocessing |
| | |
| | ## Additional Information |
| | |
| | ### Dataset Curators |
| | |
| | Created by nrodgers98 for oak defect detection research and applications. |
| | |
| | ### Licensing Information |
| | |
| | MIT License |
