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GDXray+ Dataset

2096611298
Tagsecurity
Taskobject detection
Release YearMade in 2015
Licensecustom

Introduction #

Domingo Mery, Vladimir Riffo, Uwe Zscherpelet al.

The authors introduce GDXray+: The GRIMA X-ray Database comprising 20,966 X-ray images. These images are systematically organized in a public database named GDXray+, accessible for free but exclusively for research and educational purposes. The database encompasses five categories of X-ray images: castings, welds, baggages, natural objects (nature), and settings. Each category includes several series, and each series comprises multiple X-ray images. Most series are annotated, providing either the coordinates of bounding boxes for objects of interest or labels for the images, stored in standard text files. GDXray, with a size of 3.5 GB, is available for download on the authors’ website. The authors assert that GDXray makes a valuable contribution to the X-ray testing community by offering a resource for developing, testing, and evaluating image analysis and computer vision algorithms without the need for expensive X-ray equipment. Furthermore, they emphasize its utility as a benchmark for testing and comparing different approaches on the same dataset, along with its potential use in training programs for human inspectors.

Public databases of X-ray images for medical imaging exist, but, to the best knowledge of the authors, there has not been a public database specifically for digital X-ray images in X-ray testing. In service to the X-ray testing community, the authors have collected around 21k X-ray images for the development, testing, and evaluation of image analysis and computer vision algorithms. The authors highlight that GDXray is distinct in its structure, offering a resource for various applications and presenting opportunities for diverse experiments. They elaborate on the database’s structure, providing insight into the organization of groups, series, and individual X-ray images.

Castings

The Castings group, comprising 2,727 X-ray images in 67 series, focuses on automotive parts, particularly aluminum wheels and knuckles. The authors present examples and details of each series, emphasizing that experiments on this data can be found in various publications. Notably, they highlight a series (C0001) that contains a sequence of 72 X-ray images taken from an aluminum wheel, along with annotations of bounding boxes for 226 small defects and the calibration matrix for each image.

Welds

The Welds group consists of 98 images in 3 series, taken by the BAM Federal Institute for Materials Research and Testing, Berlin, Germany. The authors showcase examples and detail each series, mentioning experiments conducted on this data and highlighting series (W0001 and W0002) that contain annotations for bounding boxes and binary images of the ground truth for 641 defects. Another series (W0003) includes 67 digitized radiographs from a round robin test on flaw recognition in welding seams, providing additional details on the data acquisition process.

Baggages

The Baggage group, containing 9,700 X-ray images in 77 series, focuses on images of various items such as backpacks, pen cases, and wallets. The authors illustrate examples and present details for each series, noting experiments conducted on this data and highlighting series (B0046, B0047, and B0048) that contain 600 X-ray images suitable for the automated detection of handguns, shuriken, and razor blades. These series include bounding box annotations for the objects of interest.

Nature

The Nature group encompasses 8,290 X-ray images in 13 series, featuring images of natural objects such as salmon filets, fruit, and wood pieces. The authors showcase examples and detail each series, emphasizing experiments conducted on this data. They highlight series (N0012 and N0013) that include annotations for bounding boxes and binary images of the ground truth for 73 fish bones. Additionally, they mention series (N0003) that provides over 7,500 labeled small crops for training purposes.

Settings

The Settings group includes 151 X-ray images in 7 series, focusing on calibration objects like checkerboards and 3D objects with regular patterns. The authors present examples and provide details for each series, noting experiments conducted on this data. They highlight series (S0001) that contains X-ray images of a copper checkerboard along with the calibration matrix for each view. Another series (S0007) can be used for modeling the distortion of an image intensifier, providing coordinates for each hole of the calibration pattern in each view along with the coordinates of the 3D model.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

GDXray+: The GRIMA X-ray Database is a dataset for object detection and classification tasks. It is used in the security industry.

The dataset consists of 20966 images with 1831 labeled objects belonging to 1 single class (object of interest).

Images in the GDXray+ dataset have bounding box annotations. There are 19834 (95% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 5 groups: baggages (9700 images), nature (8290 images), castings (2727 images), settings (151 images), and welds (98 images). Additionally, every image has its own series tag. The dataset was released in 2015 by the Pontificia Universidad Catolica de Chile, Universidad de Atacama, Chile, BAM, Germany, Universidad de Santiago de Chile, and Universidad Adolfo Ibanez.

Dataset Poster

Explore #

GDXray+ dataset has 20966 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 GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
OpenSample annotation mask from GDXray+Sample image from GDXray+
👀
Have a look at 20966 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 1 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 1-1 of 1
Class
Images
Objects
Count on image
average
Area on image
average
object of interest
rectangle
1132
1831
1.62
4.99%

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 1-1 of 1
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
object of interest
rectangle
1831
3.09%
40.49%
0.03%
9px
2.1%
853px
99.83%
85px
13.53%
9px
1.43%
553px
64.38%

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 1831 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 1831
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
object of interest
rectangle
B0063_0015.png
1712 x 2136
313px
18.28%
208px
9.74%
1.78%
2
object of interest
rectangle
B0073_0007.png
1692 x 1688
193px
11.41%
319px
18.9%
2.16%
3
object of interest
rectangle
C0054_0001.png
574 x 768
40px
6.97%
41px
5.34%
0.37%
4
object of interest
rectangle
C0054_0001.png
574 x 768
30px
5.23%
28px
3.65%
0.19%
5
object of interest
rectangle
C0045_0035.png
256 x 256
12px
4.69%
18px
7.03%
0.33%
6
object of interest
rectangle
C0045_0035.png
256 x 256
16px
6.25%
17px
6.64%
0.42%
7
object of interest
rectangle
C0030_0025.png
572 x 768
21px
3.67%
21px
2.73%
0.1%
8
object of interest
rectangle
C0030_0025.png
572 x 768
20px
3.5%
26px
3.39%
0.12%
9
object of interest
rectangle
B0001_0008.png
2208 x 2688
313px
14.18%
313px
11.64%
1.65%
10
object of interest
rectangle
C0057_0003.png
574 x 768
26px
4.53%
30px
3.91%
0.18%

License #

The X-ray images included in GDXray+ can be used free of charge, for research and educational purposes only. Redistribution and commercial use is prohibited. Any researcher reporting results which use this database should acknowledge the GDXray+ database by citing:

Mery, D.; Riffo, V.; Zscherpel, U.; Mondragón, G.; Lillo, I.; Zuccar, I.; Lobel, H.; Carrasco, M. (2015): GDXray: The database of X-ray images for nondestructive testing. Journal of Nondestructive Evaluation, 34.4:1-12.

Source

Citation #

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

Mery, D.; Riffo, V.; Zscherpel, U.; Mondragón, G.; Lillo, I.; Zuccar, I.; Lobel, H.; Carrasco, M. (2015): 
GDXray: The database of X-ray images for nondestructive testing. 
Journal of Nondestructive Evaluation, 34.4:1-12.

Source

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

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

Download #

Please visit dataset homepage to download the data.

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