Dataset Ninja LogoDataset Ninja:

CODEBRIM Dataset

159061
Tagconstruction
Taskobject detection
Release YearMade in 2019
Licensecustom

Introduction #

Released 2019-04-01 ·Martin Mundt, Sagnik Majumder, Sreenivas Muraliet al.

The CODEBRIM: COncrete DEfect BRidge IMage Dataset is designed for multi-target classification of five commonly occurring concrete defects. The dataset aims to facilitate the development of robust computer vision techniques tailored to real-world scenarios. In the pursuit of recognizing defects in concrete infrastructure, particularly in bridges, the authors of the dataset acknowledge the inherent challenges in this critical initial step for assessing structural integrity. Concrete materials exhibit significant variations in appearance, influenced by factors such as lighting conditions, weather, and diverse surface characteristics. Additionally, concrete defects often overlap, compounding the complexity of the task.

Recognizing the importance of accurately identifying various defect types concurrently, the authors emphasize the severity of overlapping defects with structural safety assessment. Concrete surfaces’ visual properties, including reflectance, roughness, and colour, vary significantly. Addressing these variations necessitates the development of computer vision methods capable of handling such complex visual environments.

The authors’ work focuses on two pivotal aspects of concrete defect recognition: the creation of a labeled multi-target dataset with overlapping defect categories and the design of specialized deep neural networks for multi-class multi-target defect classification. The CODEBRIM dataset encompasses six mutually exclusive classes: crack, spallation, exposed reinforcement bar, efflorescence (calcium leaching), corrosion stains, and non-defective background. The dataset comprises high-resolution images captured under varying environmental conditions, including wet or stained surfaces, and features diverse bridges with different deterioration levels and surface appearances.

image

(a) Top row from left to right: 1) exposed bars, spallation, cracks (hard to see); 2) hairline crack with efflorescence; 3) efflorescence; 4) defect-free concrete. Bottom row from left to right: 1) large spalled area with exposed bars and corrosion; 2) crack with graffiti; 3) corrosion stain, minor onset efflorescence; 4) defect-free concrete with dirt and markings.

image

(b) From left to right: 1) spalled area with exposed bar, advanced corrosion and efflorescence; 2) exposed corroded bar; 3) larger crack; 4) partially exposed corroded bars, cracks; 5) hairline crack; 6) heavy spallation, exposed bars, corrosion; 7) wet/damp crack with efflorescence on the top; 8) efflorescence; 9) spalled area; 10) hairline crack with efflorescence.

The acquisition of the CODEBRIM dataset stemmed from the necessity for a more comprehensive representation of overlapping defect classes, extending beyond previous research primarily focused on cracks. The authors meticulously curated and annotated the dataset, resulting in a rich resource for training and evaluating computer vision models.

The dataset encompasses:

  • 1590 high-resolution images depicting defects within the context of 30 distinct bridges,
  • 5354 annotated defect bounding boxes, often exhibiting overlapping defects, alongside 2506 generated non-overlapping background bounding boxes,
  • Varied defect counts for distinct classes: crack (2507), spallation (1898), efflorescence (833), exposed bars (1507), and corrosion stain (1559).

Note, that the number of objects in DatasetNinja may differ in comparison with the original dataset due to the conversion effects.

The multi-target nature of the dataset adds complexity compared to single-class recognition benchmarks, as many instances exhibit more than one class simultaneously. Moreover, the task presents challenges related to variations in aspect ratio, scale, and resolution across different defects and their bounding boxes. These intricacies are crucial for developing effective defect recognition algorithms, as they mirror real-world scenarios in concrete infrastructure assessment. Further details and comprehensive distributions are available in the supplementary material in the Research Paper.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

CODEBRIM: COncrete DEfect BRidge IMage Dataset is a dataset for an object detection task. It is used in the construction industry.

The dataset consists of 1590 images with 8323 labeled objects belonging to 6 different classes including crack, corrosion stain, spallation, and other: exposed bars, efflorescence, and background.

Images in the CODEBRIM dataset have bounding box annotations. There are 567 (36% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Additionally, bounding boxes with 2 or more classes are marked as duplicate bbox. Run dataset in supervisely to explore. The dataset was released in 2019 by the Goethe University, Germany and Egnatia Odos A. E., Greece.

Here are the visualized examples for the classes:

Explore #

CODEBRIM dataset has 1590 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 CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
OpenSample annotation mask from CODEBRIMSample image from CODEBRIM
👀
Have a look at 1590 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 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 1-6 of 6
Class
Images
Objects
Count on image
average
Area on image
average
crack
rectangle
629
2502
3.98
15.39%
corrosion stain
rectangle
573
1550
2.71
23.13%
spallation
rectangle
563
1892
3.36
28.44%
exposed bars
rectangle
446
1502
3.37
29.27%
efflorescence
rectangle
320
828
2.59
18.83%
background
rectangle
40
49
1.23
10.19%

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 1-6 of 6
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
crack
rectangle
2502
4.07%
99.95%
0.03%
23px
1.42%
3994px
99.97%
608px
19.53%
34px
1%
5424px
99.98%
spallation
rectangle
1892
10.47%
99.95%
0.02%
32px
1.56%
3999px
99.97%
990px
30.29%
40px
0.87%
5424px
99.98%
corrosion stain
rectangle
1550
9.63%
99.94%
0.02%
31px
1.56%
3999px
99.97%
964px
30.49%
34px
0.87%
5998px
99.98%
exposed bars
rectangle
1502
10.8%
99.94%
0.02%
31px
1.56%
3999px
99.97%
994px
30.49%
37px
0.87%
4692px
99.98%
efflorescence
rectangle
828
7.65%
99.91%
0.05%
46px
1.39%
3999px
99.97%
838px
28.08%
48px
1.75%
5424px
99.93%
background
rectangle
49
8.33%
53.92%
0.07%
76px
2.25%
3185px
92.16%
1050px
30.4%
129px
2.8%
4607px
99.98%

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 8323 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 8323
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
crack
rectangle
image_0001467.jpg
3456 x 4608
730px
21.12%
193px
4.19%
0.88%
2
crack
rectangle
image_0001467.jpg
3456 x 4608
719px
20.8%
340px
7.38%
1.54%
3
crack
rectangle
image_0001467.jpg
3456 x 4608
1272px
36.81%
266px
5.77%
2.12%
4
crack
rectangle
image_0001467.jpg
3456 x 4608
622px
18%
231px
5.01%
0.9%
5
exposed bars
rectangle
image_0001467.jpg
3456 x 4608
688px
19.91%
314px
6.81%
1.36%
6
crack
rectangle
image_0001467.jpg
3456 x 4608
1075px
31.11%
267px
5.79%
1.8%
7
crack
rectangle
image_0001467.jpg
3456 x 4608
816px
23.61%
437px
9.48%
2.24%
8
efflorescence
rectangle
image_0001467.jpg
3456 x 4608
419px
12.12%
728px
15.8%
1.92%
9
crack
rectangle
image_0001467.jpg
3456 x 4608
560px
16.2%
331px
7.18%
1.16%
10
spallation
rectangle
image_0000671.jpg
1704 x 2272
585px
34.33%
743px
32.7%
11.23%

License #

CODEBRIM license statement

The researcher has requested permission to use the CODEBRIM database at Goethe University (GU), Frankfurt Institute for Advanced Studies (FIAS), Center for Advanced Aerospace Technologies (CATEC) and Egnatia Odos A.E. (EOAE). In exchange for such permission the researcher agrees and is bound by the following terms and conditions:

  1. The CODEBRIM database comes “AS IS”. While we (GU, FIAS, CATEC, EOAE) have made every effort to ensure accuracy, no representations or warranties regarding the database are made. This includes but is not limited to warranties of non-infringement or fitness for a particular purpose and no responsibility is accepted for errors or omissions.
  2. The researcher shall use the database for non-commercial research and educational purposes only. If the researcher is employed by a for-profit, commercial entitity, the researcher’s employer shall also be bound by these terms and conditions and the researcher hereby represents full authorization to enter this agreement on behalf of such employer.
  3. The researcher may not use modified versions of the dataset or any derivative works (such as additional annotations, trained models or anything that directly includes any of the data) to procure a commercial gain.
  4. The researcher shall not distribute the database or modified versions thereof. Access to the database to fellow research associates and colleagues may be granted provided that they first agree to be bound by these terms and conditions.
  5. Derivative works such as additional annotations or trained models that do not directly include any of the data are permissible to be shared under the same license agreement for non-commercial and educational purposes.
  6. The researcher shall include a reference to the CODEBRIM database and its corresponding publication: “Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset; Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos and Visvanathan Ramesh; Conference on Computer Vision and Pattern Recognition, 2019” in any work that makes use of the database.
  7. All rights not expressly granted to the researcher are reserved by us. They reserve the right to terminate the researcher’s access to the database at any time.

Source

Citation #

If you make use of the CODEBRIM data, please cite the following reference:

@dataset{martin_mundt_2019_2620293,
  author       = {Martin Mundt and
                  Sagnik Majumder and
                  Sreenivas Murali and
                  Panagiotis Panetsos and
                  Visvanathan Ramesh},
  title        = {CODEBRIM: COncrete DEfect BRidge IMage Dataset},
  month        = apr,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.2620293},
  url          = {https://doi.org/10.5281/zenodo.2620293}
}

Source

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

@misc{ visualization-tools-for-codebrim-dataset,
  title = { Visualization Tools for CODEBRIM Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/codebrim } },
  url = { https://datasetninja.com/codebrim },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Please visit dataset homepage to download the data.

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