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Wood Defect Detection Dataset

20276102972
Tagmanufacturing
Taskinstance segmentation
Release YearMade in 2021
LicenseCC BY 4.0
Download163 GB

Introduction #

Released 2021-04-15 ·Kodytek Pavel, Bodzas Alexandra, Bilik Petr

To enhance quality control procedures in the wood industry, the authors of the Wood Defect Detection dataset propose new automated vision-based systems. The dataset encompasses over 43,000 labeled defects found on wood surfaces, encompassing ten different types of the most frequently occurring defects. These include live_knot, dead_knot, knots_with_cracks, crack, resin, marrow, quartzite, missing_knot, blue_stain, and overgrown areas. Each image within the dataset is accompanied by a semantic map and a bounding box label, facilitating both semantic segmentation and localization tasks. Notably, all the data were directly collected from a wood production line as part of the manufacturing process. They highlight the significant variability in raw materials and the intricate manufacturing processes, leading to a diverse range of observable structural defects. These defects require assessment by trained specialists through manual processes that are laborious, subject to bias, and less efficient.

The wood industry’s manufacturing processes influence material utilization and cost efficiency. The complexity and variability of wood materials can result in various defects, affecting both mechanical properties and aesthetic value. Despite increasing automation, many companies rely on trained domain experts for quality grading, with manual inspection often limited by volume and subject to human-related factors. To address this, the authors aim to develop accurate automated systems.

The authors constructed a laboratory setup comprising a conveyor belt, light source, and line scan camera. The camera captured images at a speed of 4 m/s, synchronized with the conveyor belt’s movement. This experiment involved 250 wood veneers, each scanned at a resolution of 4,000 × 3,000 pixels. The resulting dataset contained 4,729 usable images, of which 353 had wood defects. To address the lack of extensive databases in this field, the authors performed an experiment in an industrial environment to acquire authentic data from the production line. To overcome challenges such as high-speed conveyor belts and vibrations, they developed both hardware and software solutions for high-resolution image acquisition. To ensure high-quality images at a speed of 9.6 m/s, the authors employed a trilinear line scan camera and a high-performance Camera Link frame grabber.

Annotation was performed manually, with the authors developing a customizable annotation tool to streamline the process. They created BMP files representing semantic maps of labeled defects, with each zone painted in an image corresponding to a specific defect. The annotation tool automatically generated coordinates for bounding boxes and labels. This large-scale dataset and associated labeling offer a valuable resource for advancing automated wood defect detection and quality control systems.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Supporting Data for Deep Learning and Machine Vision Based Approaches for Automated Wood Defect Detection and Quality Control is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the wood and manufacturing industries, and in the computer aided quality control domain.

The dataset consists of 20276 images with 86803 labeled objects belonging to 10 different classes including live_knot, dead_knot, resin, and other: knot_with_crack, crack, marrow, quartzite, missing_knot, blue_stain, and overgrown.

Images in the Wood Defect Detection dataset have pixel-level instance segmentation and bounding box annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation task (only one mask for every class). There are 1991 (10% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2021 by the VSB TUO, Czech Republic.

Dataset Poster

Explore #

Wood Defect Detection dataset has 20276 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
OpenSample annotation mask from Wood Defect DetectionSample image from Wood Defect Detection
👀
Have a look at 20276 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 10 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 10
Class
Images
Objects
Count on image
average
Area on image
average
Live_knot
Unknown
11913
41960
3.52
1.21%
Death_know
Unknown
8350
23731
2.84
0.71%
resin
any
2625
6871
2.62
1.02%
knot_with_crack
any
1835
4380
2.39
2.59%
Crack
Unknown
1578
4306
2.73
1.83%
Marrow
Unknown
1061
2347
2.21
3.01%
Quartzity
Unknown
847
2024
2.39
5.45%
Knot_missing
Unknown
478
982
2.05
0.59%
Blue_stain
Unknown
77
192
2.49
6.84%
overgrown
any
6
10
1.67
0%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 10
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
Live_knot
Unknown
41960
0.6%
33.56%
0%
1px
0.1%
810px
79.1%
85px
8.32%
1px
0.04%
1407px
50.25%
Death_know
Unknown
23731
0.44%
22.67%
0%
1px
0.1%
648px
63.28%
75px
7.31%
1px
0.04%
1288px
46%
resin
any
6871
0.64%
29.4%
0%
1px
0.1%
1024px
100%
187px
18.24%
2px
0.07%
1397px
49.89%
knot_with_crack
any
4380
1.93%
10.58%
0%
1px
0.1%
408px
39.84%
156px
15.26%
1px
0.04%
1022px
36.5%
Crack
Unknown
4306
0.94%
12.75%
0%
1px
0.1%
1024px
100%
423px
41.29%
1px
0.04%
467px
16.68%
Marrow
Unknown
2347
2.21%
11.38%
0%
1px
0.1%
1024px
100%
543px
53.07%
2px
0.07%
518px
18.5%
Quartzity
Unknown
2024
3.7%
29.94%
0%
1px
0.1%
1024px
100%
764px
74.65%
1px
0.04%
895px
31.96%
Knot_missing
Unknown
982
0.49%
7.06%
0%
1px
0.1%
360px
35.16%
88px
8.61%
1px
0.04%
848px
30.29%
Blue_stain
Unknown
192
4.79%
19.86%
0.4%
121px
11.82%
1024px
100%
720px
70.34%
74px
2.64%
634px
22.64%
overgrown
any
10
0%
0.01%
0%
1px
0.1%
15px
1.46%
5px
0.45%
2px
0.07%
17px
0.61%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 86803 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 86803
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
knot_with_crack
any
101600016.bmp
1024 x 2800
145px
14.16%
321px
11.46%
1.32%
2
knot_with_crack
any
101600016.bmp
1024 x 2800
147px
14.36%
322px
11.5%
1.65%
3
Live_knot
Unknown
131000033.bmp
1024 x 2800
31px
3.03%
108px
3.86%
0.1%
4
resin
any
131000033.bmp
1024 x 2800
157px
15.33%
79px
2.82%
0.25%
5
resin
any
131000033.bmp
1024 x 2800
157px
15.33%
79px
2.82%
0.43%
6
Live_knot
Unknown
131000033.bmp
1024 x 2800
33px
3.22%
108px
3.86%
0.12%
7
Live_knot
Unknown
172100044.bmp
1024 x 2800
82px
8.01%
137px
4.89%
0.32%
8
Live_knot
Unknown
172100044.bmp
1024 x 2800
83px
8.11%
137px
4.89%
0.4%
9
Live_knot
Unknown
150600016.bmp
1024 x 2800
86px
8.4%
86px
3.07%
0.2%
10
Live_knot
Unknown
150600016.bmp
1024 x 2800
88px
8.59%
88px
3.14%
0.27%

License #

Supporting data for Deep Learning and Machine Vision based approaches for automated wood defect detection and quality control is under CC BY 4.0 license.

Source

Citation #

If you make use of the Wood Defect Detection data, please cite the following reference:

@dataset{kodytek_pavel_2021_4694695,
  author       = {Kodytek Pavel and
                  Bodzas Alexandra and
                  Bilik Petr},
  title        = {{Supporting data for Deep Learning and Machine 
                   Vision based approaches for automated wood defect
                   detection and quality control.}},
  month        = apr,
  year         = 2021,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4694695},
  url          = {https://doi.org/10.5281/zenodo.4694695}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-wood-defect-detection-dataset,
  title = { Visualization Tools for Wood Defect Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/wood-defect-detection } },
  url = { https://datasetninja.com/wood-defect-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Wood Defect Detection can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Wood Defect Detection', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

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