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Magnetic Tile Surface Defect Dataset

134451059
Tagmanufacturing
Taskinstance segmentation
Release YearMade in 2018
Licensecustom
Download54 MB

Introduction #

Released 2018-02-24 Β·Yibin Huang, Congying Qiu, Kui Yuan

The authors of the Magnetic Tile Surface Defect Dataset explore the significance of magnetic tiles in engines, where maintaining a productive automatic assembly line for magnetic tile manufacturing remains a fundamental challenge. It consists of 1344 images with the following defect types: blowhole, crack, fray, break, uneven (caused by the grinding process), and images with no defects.

image

Surface defect detection is crucial for filtering out subpar products, yet this process often involves significant manual effort. In Zhejiang Province, China, the largest magnetic tile production base globally, approximately three-quarters of the workforce is engaged in manual product quality inspections. In response to this, various image processing techniques, such as those based on grayscale value, gradient edge, wavelet, curvelet, and shearlet transformations, have been proposed to automate inspection tasks. Recently, deep neural network models have gained prominence for surface defect detection, demonstrating state-of-the-art performance in classification tasks. These advancements underscore the feasibility of employing computer vision for surface examinations and the potential to reduce reliance on human labor.

To delve deeper into the possibilities, authors suggest a real-time model called MCuePush U-Net, specifically designed for saliency detection of surface defect. Magnetic tiles typically feature 4–6 curved surfaces, causing image distortion. Challenges in automatic damage detection include the complexity of texture, the variety of defect shapes, and the randomness of illumination conditions, contributing to grayscale image noise. Target defects, such as blowholes, cracks, breaks, and frays, often manifest on curved surfaces without fixed patterns, introducing randomness to image detection tasks. Overcoming these challenges is essential for building robust computer vision models, particularly when aiming for real-time performance in the magnetic tile industry.

In image-based surface detection for magnetic tiles, the focus is primarily on minimizing the interference of product texture. While embedding denoising techniques like wavelet, curvelet, and shearlet transformations can effectively extract desirable features, they also lead to extended processing times, far from meeting real-time standards. Notably, the performance of experienced maintenance workers still surpasses machinery in terms of detection accuracy and efficiency. Transitioning from manual detection to automated surface inspection (ASI) requires substantial improvements in computer vision model performance.

For the dataset and evaluation metrics, a total of 1344 images are captured, and the region of interest (ROI) of magnetic tiles is cropped and classified into six datasets based on defect types: blowhole, crack, fray, break, uneven (caused by the grinding process), and free (no defects), each with pixel-level labels. To simulate real assembly line conditions, images of a given magnetic tile are collected under multiple illumination conditions.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Magnetic Tile Surface Defect Dataset is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the manufacturing industry, and in the surface defect detection domain.

The dataset consists of 1344 images with 459 labeled objects belonging to 5 different classes including blowhole, uneven, break, and other: crack and fray.

Images in the Magnetic Tile Surface Defect dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. There are 956 (71% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Additionally, every image has experiment tag. The dataset was released in 2018 by the Chinese Academy of Sciences and Columbia University, USA.

Dataset Poster

Explore #

Magnetic Tile Surface Defect dataset has 1344 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 Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
OpenSample annotation mask from Magnetic Tile Surface DefectSample image from Magnetic Tile Surface Defect
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Have a look at 1344 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 5 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 1-5 of 5
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
blowholeβž”
mask
115
116
1.01
0.23%
unevenβž”
mask
99
108
1.09
25.24%
breakβž”
mask
85
128
1.51
4.53%
crackβž”
mask
57
70
1.23
0.72%
frayβž”
mask
32
37
1.16
17.47%

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 1-5 of 5
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
break
mask
128
3.01%
23.55%
0.01%
2px
0.64%
263px
100%
59px
20.46%
3px
0.66%
263px
100%
blowhole
mask
116
0.23%
0.72%
0.03%
7px
2.13%
33px
9.38%
15px
4.85%
5px
1.76%
34px
17.54%
uneven
mask
108
23.14%
49.81%
0.01%
3px
0.85%
195px
63.11%
94px
30.68%
5px
1.15%
620px
100%
crack
mask
70
0.58%
3.36%
0.07%
6px
1.54%
268px
98.11%
78px
26.39%
6px
0.99%
40px
32.79%
fray
mask
37
15.11%
44.5%
0.11%
18px
4.84%
355px
100%
172px
59.55%
16px
3.28%
423px
100%

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 459 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 459
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”
uneven
mask
exp3_num_186885.jpg
317 x 195
187px
58.99%
177px
90.77%
46.68%
2βž”
break
mask
exp2_num_356745.jpg
378 x 504
20px
5.29%
61px
12.1%
0.43%
3βž”
blowhole
mask
exp3_num_40438.jpg
290 x 116
10px
3.45%
16px
13.79%
0.39%
4βž”
break
mask
exp4_num_348673.jpg
378 x 516
28px
7.41%
35px
6.78%
0.21%
5βž”
uneven
mask
exp3_num_24829.jpg
336 x 524
4px
1.19%
6px
1.15%
0.01%
6βž”
uneven
mask
exp3_num_24829.jpg
336 x 524
3px
0.89%
6px
1.15%
0.01%
7βž”
uneven
mask
exp1_num_28341.jpg
333 x 522
83px
24.92%
174px
33.33%
3.89%
8βž”
blowhole
mask
exp5_num_291077.jpg
356 x 295
33px
9.27%
34px
11.53%
0.46%
9βž”
blowhole
mask
exp6_num_54366.jpg
276 x 125
21px
7.61%
13px
10.4%
0.54%
10βž”
blowhole
mask
exp1_num_108889.jpg
373 x 248
23px
6.17%
9px
3.63%
0.15%

License #

It is welcomed to use our dataset. And if it is used in your research, please cite our paper.
A toolbox for surface defects saliency detection can be reach at https://github.com/abin24/Saliency-detection-toolbox. In which our MCue and 14 other saliency detection models are available.

Source

Citation #

If you make use of the Magnetic Tile Surface Defect data, please cite the following reference:

@dataset{Magnetic Tile Surface Defect,
  author={Yibin Huang and Congying Qiu and Kui Yuan},
  title={Magnetic Tile Surface Defect Dataset},
  year={2018},
  url={https://github.com/abin24/Magnetic-tile-defect-datasets.}
}

Source

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

@misc{ visualization-tools-for-magnetic-tile-surface-defect-dataset,
  title = { Visualization Tools for Magnetic Tile Surface Defect Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/magnetic-tile-surface-defect } },
  url = { https://datasetninja.com/magnetic-tile-surface-defect },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Magnetic Tile Surface Defect 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='Magnetic Tile Surface Defect', 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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