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Surface Defect Detection: Dataset & Papers <sup>📌</sup>

<p>📈 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance. Important critical papers from year 2017 have been collected and compiled, which can be viewed in the <a href="https://github.com/Charmve/Surface-Defect-Detection/tree/master/Papers">:open_file_folder: [<b><i>Papers</i></b>]</a> folder. 🐋 </p> <div align=center><img src="metal_surface.png"></div> <br> <p align="center"> Dataset download: <code><img height="20" src="https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png" alt="Google Drive" title="Google Drive"></code> <a href="https://drive.google.com/drive/folders/1q7lirc_yQBXxUSECwX1UvV1TS4eioFm8">Google Drive</a> | <code><img height="20" src="https://user-images.githubusercontent.com/29084184/127970991-fcb23d68-8369-47af-918a-fef8f0becccd.png" alt="Baidu Cloud" title="Baidu Cloud"></code> <a href="https://pan.baidu.com/s/1GWQ_acTF5BnJgpJRSw8BKA">百度云盘</a> <code>o7p5</code> </p>

Introduction

<p>At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios.</p> <p>Compared with the clear classification, detection and segmentation tasks in computer vision, the requirements for defect detection are very general. In fact, its requirements can be divided into three different levels: "what is the defect" (<strong>classification</strong>), "where is the defect" (<strong>positioning</strong>) and "How many defects are" (<strong>split</strong>).</p> <div align="center">

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Introduction
Key Issues
- Small Sample Problem
- Real-time Problem
Common Datasets
More Inventory
Papers
Acknowledgements
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Notification
Community

1. Key Issues in Surface Defect Detection

1）Small Sample Problem

<p>The current deep learning methods are widely used in various computer vision tasks, and surface defect detection is generally regarded as its specific application in the industrial field. In traditional understanding, the reason why deep learning methods cannot be directly applied to surface defect detection is because in a real industrial environment, there are too few industrial defect samples that can be provided.</p> <p>Compared with the more than 14 million sample data in the ImageNet dataset, the most critical problem faced in surface defect detection is <b>small sample problem</b>. In many real industrial scenarios, there are even only a few or dozens of defective images. In fact, for the small sample problem which is one of the key problems in industrial surface defect detection, there are currently 4 different solutions:</p>

<b>- Data Amplification and Generation</b>

<p> The most commonly used defect image expansion method is to use multiple image processing operations such as mirroring, rotation, translation, distortion, filtering, and contrast adjustment on the original defect samples to obtain more samples. Another more common method is data synthesis, where individual defects are often fused and superimposed on normal (non-defective) samples to form defective samples.</p>

<b>- Network Pre-training and Transfer Learning</b>

<p>Generally speaking, using small samples to train deep learning networks can easily lead to <strong>overfitting</strong>, so methods based on pre-training networks or transfer learning are currently one of the most commonly used methods for samples.</p>

<b>- Reasonable Network Structure Design</b>

<p>The need for samples can also be greatly reduced by designing a reasonable network structure. Based on the compressed sampling theorem to compress and expand small sample data, we use CNN to directly classify the compressed sampling data features. Compared with the original image input, compressing the input can greatly reduce the network's demand for samples. In addition, the surface defect detection method based on the twin network can also be regarded as a special network design, which can greatly reduce the sample requirement.</p>

<b>- Unsupervised or Semi-supervised Method</b>

In the unsupervised model, only normal samples are used for training, so there is no need for defective samples. The semi-supervised method can use unlabeled samples to solve the network training problem in the case of small samples.

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2）Real-time Problem

<p>The defect detection methods based on deep learning include three main links in industrial applications: <b>data annotation</b>, <b>model training</b>, and <b>model inference</b>. Real-time in actual industrial applications pays more attention to model inference. At present, most defect detection methods are concentrated in the accuracy of classification or recognition, little attention is paid to the efficiency of model inference. There are many methods for accelerating the model, such as model weighting and model pruning. In addition, although the existing deep learning model uses GPU as a general-purpose computing unit(GPGPU), with the development of technology, it is believed that FPGA will become an attractive alternative.</p>

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2. Common Datasets for Industrial Surface Defect Detection

1）Steel Surface: NEU-CLS

NEU-CLS can be used for classification and positioning tasks.

:x: Official Link：http://faculty.neu.edu.cn/yunhyan/NEU_surface_defect_database.html

<b> latest access 🔗 - (#16) </b>

<div align=center><img src="https://img-blog.csdnimg.cn/20200927223042720.png"></div> <p>The surface defect dataset released by Northeastern University (NEU) collects six typical surface defects of hot-rolled steel strips, namely rolling scale (RS), plaque (Pa), cracking (Cr), pitting surface (PS), inclusions (In) and scratches (Sc). The dataset includes 1,800 grayscale images, six different types of typical surface defects each of which contains 300 samples. For defect detection tasks, the dataset provides annotations that indicate the category and location of the defect in each image. For each defect, the yellow box is the border indicating its location, and the green label is the category score.</p> <div align=center><img src="https://user-images.githubusercontent.com/29084184/114502526-82306280-9c5e-11eb-9d60-011ee100e179.png"></div>

Kaggle - Severstal: Steel Defect Detection

Severstal is leading the charge in efficient steel mining and production. They believe the future of metallurgy requires development across the economic, ecological, and social aspects of the industry—and they take corporate responsibility seriously. The company recently created the country’s largest industrial data lake, with petabytes of data that were previously discarded. Severstal is now looking to machine learning to improve automation, increase efficiency, and maintain high quality in their production.

https://www.kaggle.com/c/severstal-steel-defect-detection

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2）Solar Panels: elpv-dataset

<p>A dataset of functional and defective solar cells extracted from EL images of solar modules.</p>

🔗 link：https://github.com/zae-bayern/elpv-dataset

The dataset contains 2,624 samples of 300x300 pixels 8-bit grayscale images of functional and defective solar cells with varying degree of degradations extracted from 44 different solar modules. The defects in the annotated images are either of intrinsic or extrinsic type and are known to reduce the power efficiency of solar modules.

All images are normalized with respect to size and perspective. Additionally, any distortion induced by the camera lens used to capture the EL images was eliminated prior to solar cell extraction.

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3）Metal Surface: KolektorSDD

The dataset is constructed from images of defected electrical commutators that were provided and annotated by Kolektor Group. Specifically, microscopic fractions or cracks were observed on the surface of the plastic embedding in electrical commutators. The surface area of each commutator was captured in eight non-overlapping images. The images were captured in a controlled environment.

Official Link:https://www.vicos.si/Downloads/KolektorSDD
Download Link：https://pan.baidu.com/share/init?surl=HSzHC1ltHvt1hSJh_IY4Jg (password：1zlb)
Implementation： https://github.com/skokec/segdec-net-jim2019

The dataset consists of:

50 physical items (defected electrical commutators)
8 surfaces per item
Altogether 399 images:<br> -- 52 images of visible defect<br> -- 347 images without any defect
Original images of sizes:<br> -- width: 500 px<br> -- height: from 1240 to 1270 px
For training and evaluation images should be resized to 512 x 1408 px

For each item the defect is only visible in at least one image, while two items have defects on two images, which means there were 52 images where the defects are visible. The remaining 347 images serve as negative examples with non-defective surfaces.

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4）PCB Inspection: DeepPCB

🔗 Download Link：https://github.com/Charmve/Surface-Defect-Detection/tree/master/DeepPCB

<div align=center><img src="https://github.com/tangsanli5201/DeepPCB/blob/master/fig/test.jpg" width="375" style="margin:20">       <img src="https://github.com/tangsanli5201/DeepPCB/blob/master/fig/template.jpg" width="375" style="margin:20"> </div> <div align=center> an example of the tested image                                         the corresponding template image </div> <p align=center>Figure 1. PCB Inspection Dataset.</p> <br>

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5）Fabric Defects Dataset: AITEX

🔗 Download Link：https://pan.baidu.com/s/1cfC4Ll5QlnwN5RTuSZ6b7w (password：b9uy)

This dataset consists of 245 4096x256 pixel images with seven different fabric structures. There are 140 non-defect images in the dataset, 20 of each type of fabric. In addition, there are 105 images of different types of fabric defects (12 types) common in the textile industry. The image size allows users to use different window sizes, thereby the number of samples can be increased. The online dataset also contains segmentation masks of all defective images, so that white pixels represent defective areas and the remaining pixels are black.

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