Surface Defect Detection: Dataset & Papers &#;

If you are looking for more details, kindly visit our website.

&#; 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 have been collected and compiled, which can be viewed in the &#; [Papers] folder. &#;

Dataset download: Google Drive | &#;&#;&#;&#; o7p5

Introduction

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).

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Table of Contents

1. Key Issues in Surface Defect Detection

1&#;Small Sample Problem

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:

- Data Amplification and Generation

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.

- Network Pre-training and Transfer Learning

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.

- Reasonable Network Structure Design

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.

- Unsupervised or Semi-supervised Method

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

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.

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2. Common Datasets for Industrial Surface Defect Detection

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

latest access &#; - (#16)

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.

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

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.

The dataset consists of:

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

an example of the tested image                                         the corresponding template image

Figure 1. PCB Inspection Dataset.

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5&#;Fabric Defects Dataset: AITEX

• &#; Download Link&#;https://pan.baidu.com/s/1cfC4Ll5QlnwN5RTuSZ6b7w (password&#;b9uy)

This dataset consists of 245 x256 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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6&#;Fabric Defect Dataset (Tianchi)

• &#; Download Link&#;https://pan.baidu.com/s/1LMbujxvr5iB3SwjFGYHspA (password&#;gat2)

In the actual production process of cloth, due to the influence of various factors, defects such as stains, holes, lint, etc. will occur. In order to ensure the quality of the product, the cloth needs to be inspected for defects.

Fabric defect inspection is an important part of the textile industry's production and quality management. At present, manual inspection is susceptible to subjective factors and lacks consistency, and inspection personnel working for a long time under strong light has a great impact on vision. Due to the wide variety of fabric defects, various morphological changes, and the difficulty of observation and recognition, the intelligent detection of fabric defects has been a technical bottleneck that has plagued the industry for many years.

This dataset covers all kinds of important defects in fabrics in the textile industry, and each picture contains one or more defects. The data includes two types of plain cloth and patterned cloth. Among them, about pieces of plain cloth data are used for preliminary matches, and about 12,000 pieces of patterned cloth data are used for semi-finals.

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7&#;Aluminium Profile Surface Defect Dataset&#;Tianchi&#;

Due to the influence of various factors in the actual production process of aluminum profile, the surface of the aluminum profile will have cracks, peeling, scratches and other defects, which will seriously affect the quality of the aluminum profile. To ensure product quality, manual visual inspection is required. However, the surface of the aluminum profile itself contains textures, which are not highly distinguishable from defects.

Traditional manual visual inspection methods have many shortcomings, which are very laborious, cannot accurately judge surface defects in time, and have difficult to control the efficiency of quality inspection. In recent years, deep learning has made rapid progress in image recognition and other fields. Aluminum profile manufacturers are eager to use the latest AI technology to innovate the existing quality inspection process, automatically complete quality inspection tasks, reduce the incidence of missed inspections, and improve product quality. AI technology, especially deep learning, makes aluminum profile product production managers completely free from the inability to fully grasp the state of product surface quality.

In the dataset of the competition, there are 10,000 pieces of monitoring image data from aluminum profiles with defects in actual production, and each image contains one or more defects. The sample image for machine learning will clearly identify the type of defect contained in the image.

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8&#;Weakly Supervised Learning for Industrial Optical Inspection&#;DAGM &#;

Dataset introduction:

• Mainly aimed at miscellaneous defects on textured backgrounds.

• Training data with weaker supervision.

• Contains ten data sets, the first six are training data sets, and the last four are test data sets.

• Each dataset contains "non-defective" images and 150 "defective" images saved in grayscale 8-bit PNG format. Each data set is generated by a different texture model and defect model.

• The background texture of the "No Defect" image shows no defect, and the background texture of the "No Defect" image has exactly one marked defect.

• All datasets have been randomly divided into training and testing sub-data sets of equal size.

• Weak labels are represented by ellipses, which roughly indicate the defect area.

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9&#;Cracks on the Surface of the Construction

CrackForest Dataset is an annotated road crack image database which can reflect urban road surface condition in general.

• Github Link&#;https://github.com/cuilimeng/CrackForest-dataset

• Download link&#;https://pan.baidu.com/s/j5QbDr7T3XQvDxAzVpg (password&#;jajn)

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

• Bridge cracks. There are images of bridge crack without pixel-level ground truth. From the authors "Liangfu Li, Weifei Ma, Li Li, Xiaoxiao Gao". Files can be reached by visiting https://github.com/Charmve/Surface-Defect-Detection/tree/master/Bridge_Crack_Image.

• Crack on road surface. From Shi Yong, and Cui Limeng and Qi Zhiquan and Meng Fan and Chen Zhensong. Original dataset can be reached at https://github.com/Charmve/Surface-Defect-Detection/tree/master/CrackForest. We extract the image files of the pixel level ground truth.

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10&#;Magnetic Tile Dataset

Magnetic tile dataset by githuber: abin24, which can be downloaded from https://github.com/Charmve/Surface-Defect-Detection/tree/master/Magnetic-Tile-Defect, which was used in their paper "Surface defect saliency of magnetic tile", the paper can be reach by here or here

Figure 3. An overview of our dataset.

This is also the datasets of the paper "Saliency of magnetic tile surface defects". The images of 6 common magnetic tile defects were collected, and their pixel level ground-truth were labeled.

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11&#;RSDDs: Rail Surface Defect Datasets

The RSDDs dataset contains two types of datasets: the first is a type I RSDDs dataset captured from the fast lane, which contains 67 challenging images. The second is a Type II RSDDs dataset captured from a normal/heavy transportation track, which contains 128 challenging images.

Each image of the two data sets contains at least one defect, and the background is complex and noisy.

These defects in the RSDDs dataset have been marked by professional human observers in the field of track surface inspection.

• Official Link&#;http://icn.bjtu.edu.cn/Visint/resources/RSDDs.aspx

• Download Link&#;https://pan.baidu.com/share/init?surl=svsnqL0r1kasVDNjppkEwg (password&#;nanr)

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12&#;Kylberg Texture Dataset v.1.0

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

Short description

• 28 texture classes, see Figure 4.
• 160 unique texture patches per class. (Alternative dataset with 12 rotations per each original patch, 160*12= texture patches per class).
• Texture patch size: 576x576 pixels.
• File format: Lossless compressed 8 bit PNG.
• All patches are normalized with a mean value of 127 and a standard deviation of 40.
• One directory per texture class.
• Files are named as follows: blanket1-d-p011-r180.png, where blanket1 is the class name, d original image sample number (possible values are a, b, c, or d), p011 is patch number 11, r180 patch rotated 180 degrees.

&#; Offical Link: http://www.cb.uu.se/~gustaf/texture/

Eastloong contains other products and information you need, so please check it out.

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13&#;KTH-TIPS database

Repeat the background texture data set, the sample picture is as follows

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14&#;Escalator Step Defect Dataset

&#; Offical Link&#;https://aistudio.baidu.com/aistudio/datasetdetail/

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15&#;Transmission Line Insulator Dataset

In the data set, Normal_Insulators contains 600 insulator images captured by drones. Defective_Insulators contains defective insulators, and the number of defective images of insulators is 248. The data set includes data sets and labels.

&#; Offical Link&#;https://github.com/InsulatorData/InsulatorDataSet

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16&#;MVTEC ITODD

The MVTec Industrial 3D Object Detection Dataset (MVTec ITODD) is a public dataset for 3D object detection and pose estimation with a strong focus on industrial settings and applications.

The dataset consists of

• 28 objects and labeled scenes containing instances of these objects
• Five sensors (two 3D sensors and three grayscale cameras) observing each scene

More information can be found in this PDF file &#;.

&#; Download link https://www.mvtec.com/company/research/datasets/mvtec-itodd

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17&#;BSData - dataset for Instance Segmentation and industrial Wear Forecasting

The dataset contains channel 3 images with 394 image-annotations for the surface damage type &#;pitting&#;. The annotations made with the annotation tool labelme, are available in JSON format and hence convertible to VOC and COCO format. All images come from two BSD types.

The other BSD type is shown on 325 images with two image-sizes. Since all images of this type have been taken with continuous time the degree of soiling is evolving.

Also, the dataset contains as above mentioned 27 pitting development sequences with every 69 images.

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

&#; Offical link https://github.com/2Obe/BSData

Sincerely, thank @Beñat Gartzia for his recommendation and all your attention!

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18&#;The Gear Inspection Dataset

The Gear Inspection Dataset (GID) is a dataset for a competition held by Baidu (China) Co., called the "National Artificial Intelligence Innovation Application Competition." It has two thousand grayscale images with annotations for three types of defects from a real-world source. Each picture includes defects described in a separate JSON file with the image name, label categories, bounding boxes, and polygons for segmentation. Nevertheless, the tags for labeling categories do not include specific information about their type but only numbers, so spotting their similarities with other related datasets is challenging.

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

&#; Offical link http://www.aiinnovation.com.cn/#/dataDetail?id=34

• Download Link&#;

  • Gear Detection Training Dataset: https://pan.baidu.com/s/17HoFfBUQGeX7G0ibkPExrw (passwprd: hm7k)
  • Gear detection A list evaluation dataset: https://pan.baidu.com/s/157Zf7hcTM78GhXtXI5ySFQ (pass: 2R6K)
  • Gear detection B list evaluation dataset: https://pan.baidu.com/s/1OjOZotqlRSvsYLA_qH2nXA (pass: hypd)

• Mirrors:

  • Gear Detection Training Dataset: https://drive.google.com/file/d/1CZo-Ab5BXkTjV-b1-NIFzYMjfJQMl4nG/view?usp=share_link
  • Gear detection A list evaluation dataset: https://drive.google.com/file/d/1-0sSrmhElBseeZWICu77lzTxoOiRD8yG/view?usp=share_link
  • Gear detection B list evaluation dataset: N/A.

Note: The contest dataset is not for commercial use.

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19&#;AeBAD Aircraft Engine Blade Anomaly Detection

Download link: http://suo.nz/2IU48P

The real-world aero-engine blade anomaly detection (AeBAD) data set consists of two sub-data sets: the single blade data set (AeBAD-S) and the blade video anomaly detection data set (AeBAD-V). Compared with existing datasets, AeBAD has the following two characteristics: 1.) The target samples are not aligned and at different scales. 2.) There is a domain shift in the distribution of normal samples in the test set and training set, where the domain shift is mainly caused by changes in illumination and view.

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20&#;BeanTech Anomaly Detection(BTAD)

Download Link&#;http://suo.nz/2JEGEi

The BTAD (BeanTech Anomaly Detection) dataset is a real-world industrial anomaly dataset. This dataset contains a total of real-world images of 3 industrial products.

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3. More Inventory of the Best Data Set Sources

I have been collecting surface defect detection data sets, but there are still many data sets that have not been collected. For the data sets not collected in this repo, you can go to the following sites to view. At the same time, everyone is very welcome to share the new data set and become the contributor of this repo.

source url Recommended Kaggle https://www.kaggle.com/datasets &#;&#;&#;&#;&#; Paper With Code https://paperwithcode.com/sota &#;&#;&#;&#;&#; Registry of Open Data on AWS https://registry.opendata.aws &#;&#;&#; Microsoft Research Open Data https://msropendata.com &#;&#;&#; Awesome-public-datasets https://github.com/awesomedata/awesome-public-datasets &#;&#;

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4. Surface Defect Detection Papers

I have collected some articles on surface defect detection. The main objects to be tested are: defects or abnormal objects such as metal surfaces, LCD screens, buildings, and power lines. The methods are mainly classified method, detection method, reconstruction method and generation method. The electronic version (PDF) of the paper is placed under the file named corresponding to the date in the 'Paper' folder.

Go to &#; [Papers].

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Acknowledgements

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&#;&#;&#;&#;&#;(AI_SuanFa)", which prompted me to organize a more comprehensive data set. The collection of papers comes from a CSDNer named "&#;&#;&#;&#;&#;&#;". 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.

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Download

• Download ZIP, click here
  or run git clone https://github.com/Charmve/Surface-Defect-Detection.git in the terminal
  
• Chinese Mainland - &#;&#;&#;&#;&#;&#;&#;&#; https://pan.baidu.com/s/122WY8F5VKqm3qMirqebRQw &#;&#;&#;:i20n

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Notification

This work is originally contributed by lots of great man for their paper work or industry application. You can only use this dataset for research purpose.

If you have any questions or idea, please let me know &#;

&#; Community

• Github discussions &#; or issues &#;

• QQ Group: (password&#;&#;&#;&#;)

• WeChat Group ID: Yida_Zhang2

• : yidazhang1#gmail.com

   Supporting

     Support this project by becoming a sponsor. Your name and/or logo will show up our homepage with a link to your website. &#;

Citation

Use this bibtex to cite this repository:

@misc{Surface Defect Detection,
  title={Surface Defect Detection: Dataset and Papers},
  author={Charmve},
  year={.09},
  publisher={Github},
  journal={GitHub repository},
  howpublished={\url{https://github.com/Charmve/Surface-Defect-Detection}},
}

Stargazers over time

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

&#;Star this repository&#;

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

* Update on Sep 17, @Charmve, Star and Fork

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

The data that support the findings of this study are available from the authors upon reasonable request.

After injection molding, plastic gears often exhibit surface defects, including those on end faces and tooth surfaces. These defects encompass a wide range of types and possess complex characteristics, which pose challenges for inspection. Current visual inspection systems for plastic gears suffer from limitations such as single-category defect inspection and low accuracy. There is an urgent industry need for a comprehensive and accurate method and system for inspecting defects on plastic gears, with improved inspection capability and higher accuracy. This paper presents an intelligent inspection algorithm network for plastic gear defects (PGD-net), which effectively captures subtle defect features at arbitrary locations on the surface compared to other models. An adaptive sample weighting method is proposed and integrated into an improved Focal-IoU loss function to address the issue of low inspection accuracy caused by imbalanced defect dataset distributions, thus enhancing the regression accuracy for difficult defect categories. CoordConv layers are incorporated into each inspection head to improve the model&#;s generalization capability. Furthermore, a dataset of plastic gear surface defects comprising 16 types of defects is constructed, and our algorithm is trained and tested on this dataset. The PGD-net achieves a comprehensive mean average precision (mAP) value of 95.6% for the 16 defect types. Additionally, an online inspection system is developed based on the PGD-net algorithm, which can be integrated with plastic gear production lines to achieve online full inspection and automatic sorting of plastic gear defects. The entire system has been successfully applied in plastic gear production lines, conducting daily inspections of over 60,000 gears.

1. Introduction

During the manufacturing process, plastic gears can develop a wide variety of surface defects due to issues related to materials, equipment, and processes [1,2]. As illustrated in , common defects include flash, dark spots, void, point gate perforation, point gate protrusion, white, overflow, and burn, occurring on both end faces and tooth surfaces. Based on the causes of defect formation, they can be categorized into the following four types:

• (1)

  Injection molding defects caused by the molding process, such as flow marks, white, and burn;

• (2)

  Injection defects related to the storage or use of materials, including color variations and foreign particle inclusions (such as dark spots);

• (3)

  Injection defects caused by maintenance issues or poor mold design, such as flash, bubbles, overflow, oil contamination, and short shot;

• (4)

  Defects resulting from improper human operations, such as scratches from manually removing flash and tooth surface damage from mishandling.

In response to the vast production volume and diverse surface defects of plastic gears, there is a pressing need within the plastic gear industry for an online automated inspection device, as traditional gear measuring instruments fail to meet this demand. Therefore, visual online inspection technology for gears has rapidly developed, offering advantages such as non-contact operation, high inspection efficiency, and comprehensive information acquisition, which facilitates integration with automated equipment to achieve online inspection of plastic gear defects [3,4,5].

Traditional defect inspection algorithms often struggle to accurately distinguish the complex surface defect features of plastic gears. With the rapid development of deep learning technology, defect inspection algorithms based on deep learning have shown significant improvements in inspecting multiple complex defects. However, challenges still exist, such as the real-time problem, small sample problem, small target problem, and unbalanced sample problem [6,7,8,9,10,11,12,13].

Sun et al. [14] proposed a cascade inspection method for surface defects based on high-resolution inspection images. By introducing Euclidean distance width deviation in the CIoU localization loss function, they effectively inspected wireframe defects of multiple scales, achieving mAP of 85.01% and an inference time of 37 ms. The method attained inspection accuracy exceeding 95%, with a false negative rate controlled below 6%, but only inspected three types of defects: dirt, white scratches, and black scratches. He et al. [15] introduced a novel steel plate defect inspection system. The system employed a baseline convolutional neural network to generate feature maps at each stage, and a multi-layer feature fusion network (MFN) to merge multiple hierarchical features into one, enhancing the feature&#;s ability to capture position details of more defects. The study established the NEU-DET defect inspection dataset, demonstrating accuracy rates of 99.67% for defect classification and 82.3% mAP for defect inspection. However, the system could only inspect six types of steel plate defects.

In recent years, some scholars have conducted research on gear defect inspection algorithms in the field of deep learning. Zhang et al. [16] proposed an improved YOLO-v3 network to inspect stain and miss defects in plastic gears, achieving a false inspection rate of 1.3%. However, this study only considered two types of end-face defects and did not address tooth surface defects. Kamal et al. [17] employed two classification methods based on convolutional neural networks to identify scratches, protrusions, hole erosion, and asymmetric block defects in gears. The first method had a shorter processing time of 0.09 s, with an accuracy of 92%, while the second method achieved a higher accuracy of 96.5% with an average time of 0.67 s. Nonetheless, the study focused on inspecting defects in metal gears, which has unknown effectiveness for plastic gear inspection. Xi et al. [18] developed an integrated Yolov5-Deeplabv3+ real-time segmentation network for online measurement of pitting defects in metal gears, addressing sample imbalance issues. However, the study only considered one type of pitting defect in metal gears and lacked experimental results for mixed defect inspection. Xiao et al. [19] designed an improved GA-PSO algorithm for identifying four types of defects in powder metallurgy gears: tooth breakage, wear, cracking, and scratching, achieving an accuracy rate of over 94%. However, tooth surface defect inspection was not addressed.

In summary, existing deep learning algorithms for gear defect inspection still face challenges such as limited defect inspection types, low accuracy, and insufficient research on tooth surface defect inspection.

Furthermore, apart from defect inspection algorithms, it is equally important to design the structure and control system of a gear defect inspection system. Most defect inspection systems are unsuitable for adopting vibration plate feeding methods [20,21], which will cause significant damage to plastic gears. Gear defect inspection systems can be classified into two types: integrated on conveyors and independently mounted on glass turntables. Reference [22] describes gear visual online inspection devices located above conveyors, which occupy less space, but have many factors resulting in low inspection accuracy, e.g., unstable conveyor motion, susceptibility to wear, and variable product placement angles. Additionally, the non-transparency of conveyors prevents image capture of the gear bottom surface. Reference [23] introduces a glass turntable-based injection-molded gear online inspection and sorting system, for inspecting surface defects such as dark spots and flash without flipping the product to obtain bottom-side inspection images. However, the device inspects fewer types of defects and cannot collect tooth surface defect data.

All in all, plastic gear defect online inspection systems face three main challenges: First, the diversity and complexity of plastic gear defects, encompassing both end-face and tooth surface defects, pose significant inspection difficulties, with a lack of research on specialized multi-class defect fusion inspection algorithms for plastic gears. Second, research on plastic gear defect online inspection devices is scarce, so that the industry lacks practical solutions for coupling online inspection equipment with plastic gear production lines. Third, it poses significant challenges to ensure the normal operation and coordination of various modules because integrating defect inspection algorithms with automated inspection equipment requires comprehensive consideration of mechanical structure, control systems, and inspection algorithms, involving multiple complex technologies [24].

To address the above issues, this study developed a method and system for inspecting comprehensive surface defects on plastic gears, enabling intelligent online inspection of 16 types of surface defects on plastic gears. The system has been successfully deployed in production lines. In the following, we will discuss it point by point.

The company is the world’s best High efficiency fully automatic online surface defect detection equipment supplier. We are your one-stop shop for all needs. Our staff are highly-specialized and will help you find the product you need.