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GC10-DET Dataset

2300101
Tagmanufacturing
Taskobject detection
Release YearMade in 2020
LicenseCC BY 4.0
Download919 MB

Introduction #

Released 2020-03-11 Β·Xiaoming Lv, Fajie Duan, Jia-jia Jianget al.

C10-DET represents a dataset designed for the detection of defects on large-scale metallic surfaces. It poses significant challenges in terms of the variety of defect categories, the number of images, and data volume. The presence of surface defects on metallic materials can have detrimental effects on the quality of industrial products, making efficient detection of metallic defects essential to meet the quality standards set by various industries. Consequently, there has been a growing interest in the field of metallic surface defect detection, leading to substantial advancements in quality control for industrial applications. However, the task of identifying metallic surface defects is inherently complex, primarily due to environmental factors like lighting, light reflections, and the unique properties of metal materials. These factors significantly increase the intricacy of surface defect detection. Note, that while the original paper stated that the dataset contained 3,570 grayscale images, the current version offers 2,300 images.

Existing defect datasets are limited in terms of data scale and defect diversity, often containing only a few categories. The small dataset size can result in detection models with weak robustness and limited generalization capabilities, especially when dealing with complex industrial scenarios. To address this issue and provide a benchmark that closely resembles realistic scenarios, the authors constructed a new dataset called GC10-DET.

The data collection system utilized a set of linear array CCD cameras equipped with a direct current (DC) light source to eliminate the presence of alternating current (AC) stripes. For certain production lines, like hot-rolled strip production lines, which achieve speeds of up to 10 m/s, high-speed linear CCD cameras were used to improve detection speed and image resolution. In the case of wide-format steel plates, 4096-pixel line scan CCD cameras were stitched together to capture complete images. The captured steel plate images were then transmitted to a server with ample computing resources for defect detection. The results were output to the console for quality control.

image

The industrial system consists of host computers, production lines, servers, and detection results. The host computer is to control the operation of the entire system while the server is to deploy a defect detection model for the production line. Finally, detection results provide feedback for the production line.

The authors provided detailed information about the brands, parameters, and types of equipment used for data collection. The cameras used were Teledyne DALSA LA-CM-04K08A models, equipped with Moritex ML-3528-43F lenses, and had a pixel size of 7.04 Β΅m Γ— 7.04 Β΅m. The server utilized 32GB of running memory and NVIDIA RTX 2082ti GPU cards.

Each defect type is described in detail, explaining how it appears on the steel strip surface and the reasons behind its occurrence:

  • Punching: In the production line of the strip, the steel strip needs to be punched according to the product specifications; mechanical failure may lead to unwanted punching, resulting in punching defects.
  • Welding line: When the strip is changed, it is necessary to weld the two coils of the strip, and the weld line is produced. Strictly speaking, this is not a defect, but it needs to be automatically detected and tracked to be circumvented in subsequent cuts.
  • Crescent gap: In the production of steel strip, cutting sometimes results in defects, just like half a circle.
  • Water spot: A water spot is produced by drying in production. Under different products and processes, the requirements for this defect are different. However, because the water spots are generally with low contrast, and are similar to other defects such as oil spots, they are usually detected by mistake.
  • Oil spot: An oil spot is usually caused by the contamination of mechanical lubricant, which will affect the appearance of the product.
  • Silk spot: A local or continuous wave-like plaque on a strip surface that may appear on the upper and lower surfaces, and the density is uneven in the whole strip length direction. Generally, the main reason lies in the uneven temperature of the roller and uneven pressure.
  • Inclusion: Inclusion is a typical defect of metal surface defects, usually showing small spots, fish scale shape, strip shape, block irregular distribution in the strip of the upper and lower surface (global or local), and is often accompanied by rough pockmarked surfaces. Some inclusions are loose and easy to fall off and some are pressed into the plate.
  • Rolled pit: Rolled pits are periodic bulges or pits on the surface of a steel plate that are punctate, flaky, or strip-like. They are distributed throughout the strip length or section, mainly caused by work roll or tension roll damage.
  • Crease: A crease is a vertical transverse fold, with regular or irregular spacing across the strip, or at the edge of the strip. The main reason is the local yield along the moving direction of the strip in the uncoiling process.
  • Waist folding: There are obvious folds in the defect parts, a little more popular, a little like wrinkles, indicating that the local deformation of the defect is too large. The reason is due to low-carbon.
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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

GC10-DET: Metallic Surface Defect Detection is a dataset for an object detection task. It is used in the surface defect detection domain.

The dataset consists of 2300 images with 3563 labeled objects belonging to 10 different classes including silk_spot, welding_line, punching_hole, and other: water_spot, crescent_gap, oil_spot, inclusion, waist folding, crease, and rolled_pit.

Images in the GC10-DET dataset have bounding box annotations. There are 8 (0% 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 2020 by the Tianjin University, China.

Here is the visualized example grid with annotations:

Explore #

GC10-DET dataset has 2300 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 GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
OpenSample annotation mask from GC10-DETSample image from GC10-DET
πŸ‘€
Have a look at 2300 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
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
silk_spotβž”
rectangle
734
884
1.2
16.7%
welding_lineβž”
rectangle
512
513
1
5.91%
punching_holeβž”
rectangle
329
329
1
0.31%
water_spotβž”
rectangle
310
354
1.14
7.39%
crescent_gapβž”
rectangle
264
265
1
6.37%
oil_spotβž”
rectangle
250
569
2.28
3.41%
inclusionβž”
rectangle
201
347
1.73
1.57%
waist foldingβž”
rectangle
140
143
1.02
40.47%
creaseβž”
rectangle
53
74
1.4
8.45%
rolled_pitβž”
rectangle
46
85
1.85
5.55%

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
silk_spot
rectangle
884
13.88%
83.35%
0.31%
81px
8.1%
991px
99.1%
592px
59.19%
59px
2.88%
2009px
98.1%
oil_spot
rectangle
569
1.5%
21.1%
0.07%
29px
2.9%
999px
99.9%
168px
16.78%
26px
1.27%
758px
37.01%
welding_line
rectangle
513
5.89%
12.07%
0.17%
27px
2.7%
125px
12.5%
76px
7.61%
129px
6.3%
2047px
99.95%
water_spot
rectangle
354
6.48%
39.6%
0.14%
45px
4.5%
999px
99.9%
422px
42.21%
55px
2.69%
1398px
68.26%
inclusion
rectangle
347
0.91%
8.92%
0.04%
17px
1.7%
977px
97.7%
151px
15.11%
25px
1.22%
731px
35.69%
punching_hole
rectangle
329
0.31%
0.86%
0.03%
24px
2.4%
110px
11%
61px
6.1%
15px
0.73%
192px
9.38%
crescent_gap
rectangle
265
6.35%
25.81%
0.21%
72px
7.2%
999px
99.9%
423px
42.25%
24px
1.17%
666px
32.52%
waist folding
rectangle
143
39.62%
80.2%
3.73%
105px
10.5%
976px
97.6%
785px
78.49%
202px
9.86%
1960px
95.7%
rolled_pit
rectangle
85
3.01%
32.72%
0.22%
50px
5%
982px
98.2%
194px
19.39%
55px
2.69%
1856px
90.62%
crease
rectangle
74
6.05%
34.97%
0.13%
36px
3.6%
856px
85.6%
160px
16.04%
50px
2.44%
2031px
99.17%

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 3563 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 3563
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
welding_line
rectangle
img_02_436149500_00939.jpg
1000 x 2048
84px
8.4%
2043px
99.76%
8.38%
2βž”
welding_line
rectangle
img_02_425622400_00001.jpg
1000 x 2048
67px
6.7%
1942px
94.82%
6.35%
3βž”
crescent_gap
rectangle
img_02_425622400_00001.jpg
1000 x 2048
538px
53.8%
231px
11.28%
6.07%
4βž”
crescent_gap
rectangle
img_01_425502200_00018.jpg
1000 x 2048
266px
26.6%
213px
10.4%
2.77%
5βž”
welding_line
rectangle
img_01_425502200_00018.jpg
1000 x 2048
60px
6%
1054px
51.46%
3.09%
6βž”
punching_hole
rectangle
img_03_3403400200_00567.jpg
1000 x 2048
53px
5.3%
107px
5.22%
0.28%
7βž”
inclusion
rectangle
img_08_425506600_00952.jpg
1000 x 2048
102px
10.2%
45px
2.2%
0.22%
8βž”
welding_line
rectangle
img_03_436164700_00001.jpg
1000 x 2048
68px
6.8%
2045px
99.85%
6.79%
9βž”
punching_hole
rectangle
img_03_436164700_00001.jpg
1000 x 2048
27px
2.7%
116px
5.66%
0.15%
10βž”
punching_hole
rectangle
img_06_425608200_00007.jpg
1000 x 2048
55px
5.5%
104px
5.08%
0.28%

License #

GC10-DET: Metallic Surface Defect Detection is under CC BY 4.0 license.

Source

Citation #

If you make use of the GC10-DET data, please cite the following reference:

@dataset{GC10-DET,
	author={Xiaoming Lv and Fajie Duan and Jia-jia Jiang and Xiao Fu and Lin Gan},
	title={GC10-DET: Metallic Surface Defect Detection},
	year={2020},
	url={https://www.kaggle.com/datasets/alex000kim/gc10det}
}

Source

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

@misc{ visualization-tools-for-gc10-det-dataset,
  title = { Visualization Tools for GC10-DET Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/gc10-det } },
  url = { https://datasetninja.com/gc10-det },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset GC10-DET 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='GC10-DET', 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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