📈 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 :open_file_folder: [Papers] folder. 🐋

Dataset download: Google Drive | 百度云盘 o7p5

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.

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" (classification), "where is the defect" (positioning) and "How many defects are" (split).

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.

Compared with the more than 14 million sample data in the ImageNet dataset, the most critical problem faced in surface defect detection is small sample problem. 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:

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.

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

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.

The defect detection methods based on deep learning include three main links in industrial applications: data annotation, model training, and model inference. 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.

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.

A dataset of functional and defective solar cells extracted from EL images of solar modules.

     

Figure 1. PCB Inspection Dataset.

Figure 2. Cracks on the Bridge(left) and Cracks on the Road Surface.

Figure 3. An overview of our dataset.

Figure 4. Example patches from each one of the 28 texture classes.

Figure 5. On the left image-examples, on the right associated PNG-Annotations.

Figure 6. Examples of validation test images and their labels.

You can see this repo now, we should be grateful to the people who originally open sourced the above data set. They have brought great help to our study and research work. The idea of collecting this data set originally came from reading an article on surface defect detection by SFXiang of "AI算法修炼营(AI_SuanFa)", which prompted me to organize a more comprehensive data set. The collection of papers comes from a CSDNer named "庆志的小徒弟". These papers are only until November 19, and I will continue to be improved after that. Hopefully, feel free to CONTRIBUTE.

Finally, I want to thank the open source contributors of the above data set again.

Feel free to ask any questions, open a PR if you feel something can be done differently!

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Created by Charmve & maiwei.ai Community | Deployed on Kaggle