PCB Defect - Dataset Ninja

Introduction #

Released 2019-02-22 ·Weibo Huang, Peng Wei

PCB Defect is a dataset tailored for an object detection task, encompassing 1386 images annotated with 2953 labeled objects across six distinct classes: open_circuit, short, spurious_copper, and other nuanced defects such as missing_hole, mouse_bite, and spur. Specifically curated for Tiny Defect Detection (TDD), the dataset is instrumental for advancing quality control measures in the production of printed circuit boards (PCBs), which is a fundamental and crucial aspect of manufacturing processes in the electronics industry.

To ensure the representativeness of the dataset, authors build a PCB image acquisition system that resembles the practical AOI system used in inspection process.

The PCB image acquisition system consisting of light source, workbench, support, camera and image process unit.

The image of template board is captured by a 16-megapixel HD industrial camera equipped with CMOS sensor, and it can be controlled by computer software or a remote control. In order to adapt to different PCB sizes and avoid edge distortion, an undistorted zoomable industrial lens is also mounted, the focal length can be adjusted between 6-12 mm and the maximum aperture is f1.6. Light source is also a key part of AOI, to avoid specular reflection of the board, possible shadows and minimize the effects of uneven illumination on subsequent steps, two frosted ring LED source equipped with special diffuse matting board are introduced to effectively overcome the adverse effects of illumination. The resolution of original photo is 4608×3456 pixels, which will be adjusted according to the size of each board when make defects.

After getting cropped image, authors make 6 types of defects by photoshop, which is a graphics editor published by Adobe Systems. The defects authors defined are: missing hole, mouse bite, open circuit, short, spur, spurious copper. Each image in the dataset has 3 to 5 defects of the same category in different places. Besides, authors provide bounding box and coordinate information for every defect in every image, which is convenient for other researchers to know where the defect is. On some inspection platforms, PCB can be fixed by mechanical devices to maintain good position. However, on the assembly line, without fixing equipments, the position and the angle of the test PCB in the taken photo may distinguish from each other. Given this circumstance, in addition to the defects images with the same position as the templates, authors also provide images with random orientations to represent the situation where the image is not appropriately placed in practical detection process. The angular difference between each image and the corresponding template image is also given so that the designing and evaluating of registration algorithm could be implemented on these images.

Samples of the PCB with defects in the dataset, (a) is the defects image with the same position as template, (b) is the image with random orientation.

Moreover, rotation split has PCB images with orientations, and rotation angles

Type of Defects	Number of Images	Number of Defects
Missing Hole	115	497
Mouse Bite	115	492
Open Circuit	116	482
Short	116	491
Spur	115	488
Spurious Copper	116	503
Total	693	2953

Also, you can check the Augmented PCB Defect available on DatasetNinja based on this dataset.

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Research Paper

Kaggle

Summary #

PCB Defect is a dataset for an object detection task. It is used in the manufacturing industry, and in the damage detection domain.

The dataset consists of 1386 images with 2953 labeled objects belonging to 6 different classes including open_circuit, short, spurious_copper, and other: missing_hole, mouse_bite, and spur.

Images in the PCB Defect dataset have bounding box annotations. There are 693 (50% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: ds (693 images) and rotation (693 images). Also, the dataset includes subfolder and angle tags for rotation slit. The dataset was released in 2019 by the Peking University.

Explore #

PCB Defect dataset has 1386 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.

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Have a look at 1386 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.

Rows 1-6 of 6

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
spurious_copper➔ rectangle	116	503	4.34	0.43%
short➔ rectangle	116	491	4.23	0.55%
open_circuit➔ rectangle	116	482	4.16	0.2%
spur➔ rectangle	115	488	4.24	0.39%
mouse_bite➔ rectangle	115	492	4.28	0.3%
missing_hole➔ rectangle	115	497	4.32	0.37%

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.

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
spurious_copper rectangle	503	0.1%	0.33%	0.03%	26px	1.14%	216px	8.77%	78px	3.68%	30px	0.99%	284px	9.29%
missing_hole rectangle	497	0.09%	0.38%	0.03%	39px	1.78%	141px	6.75%	69px	3.27%	40px	1.35%	140px	6.13%
mouse_bite rectangle	492	0.07%	0.2%	0.02%	27px	1.17%	121px	5.73%	62px	2.89%	26px	0.86%	123px	4.69%
short rectangle	491	0.13%	0.83%	0.03%	35px	1.51%	174px	8.18%	80px	3.77%	34px	1.12%	243px	10.85%
spur rectangle	488	0.09%	0.31%	0.02%	25px	1.08%	155px	7.39%	70px	3.25%	31px	1.02%	159px	6.97%
open_circuit rectangle	482	0.05%	0.18%	0.01%	24px	1.04%	93px	4.61%	51px	2.37%	25px	0.82%	110px	4.91%

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.

Objects #

Table contains all 2953 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.

Rows 1-10 of 2953

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	spurious_copper rectangle	12_spurious_copper_02.jpg	2530 x 2529	112px	4.43%	52px	2.06%	0.09%
2➔	spurious_copper rectangle	12_spurious_copper_02.jpg	2530 x 2529	45px	1.78%	119px	4.71%	0.08%
3➔	spurious_copper rectangle	12_spurious_copper_02.jpg	2530 x 2529	41px	1.62%	94px	3.72%	0.06%
4➔	spurious_copper rectangle	12_spurious_copper_02.jpg	2530 x 2529	87px	3.44%	54px	2.14%	0.07%
5➔	spurious_copper rectangle	12_spurious_copper_02.jpg	2530 x 2529	85px	3.36%	53px	2.1%	0.07%
6➔	mouse_bite rectangle	11_mouse_bite_07.jpg	2248 x 2282	66px	2.94%	82px	3.59%	0.11%
7➔	mouse_bite rectangle	11_mouse_bite_07.jpg	2248 x 2282	88px	3.91%	104px	4.56%	0.18%
8➔	mouse_bite rectangle	11_mouse_bite_07.jpg	2248 x 2282	92px	4.09%	91px	3.99%	0.16%
9➔	mouse_bite rectangle	11_mouse_bite_07.jpg	2248 x 2282	58px	2.58%	88px	3.86%	0.1%
10➔	mouse_bite rectangle	11_mouse_bite_07.jpg	2248 x 2282	50px	2.22%	61px	2.67%	0.06%

License #

License is unknown for the PCB Defect dataset.

Source

Citation #

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

@misc{huang2019pcb,
  title={A PCB Dataset for Defects Detection and Classification}, 
  author={Weibo Huang and Peng Wei},
  year={2019},
  eprint={1901.08204},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Source

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

@misc{ visualization-tools-for-pcb-defect-dataset,
  title = { Visualization Tools for PCB Defect Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/pcb-defect } },
  url = { https://datasetninja.com/pcb-defect },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-01 },
}

Download #

Please visit dataset homepage to download the data.

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