Deep Learning and Machine Vision Based Inspection of Rail Surface Defects

Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning

10.20944/preprints201904.0322.v1 ◽

2019 ◽

Author(s):

Ruofeng Wei ◽

Yunbo Bi

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Defect Detection ◽

Visual Inspection ◽

Surface Defects ◽

Average Precision ◽

Saliency Maps ◽

Aluminum Profile ◽

Safety And Reliability ◽

Aluminum Profiles

Aluminum profile surface defects can greatly affect the performance, safety and reliability of products. Traditional human-based visual inspection is low accuracy and time consuming, and machine vision-based methods depend on hand-crafted features which need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, 43.3% average precision(AP) for the ten defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.

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Machine vision problem for fast recognition of surface defects of thermoelectric cooler components based on deep learning method

Journal of Physics Conference Series ◽

10.1088/1742-6596/2003/1/012007 ◽

2021 ◽

Vol 2003 (1) ◽

pp. 012007

Author(s):

Z Q Yu ◽

M Zhao ◽

J L Huang ◽

T X Wen ◽

T D Liao

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Surface Defects ◽

Thermoelectric Cooler ◽

Learning Method ◽

Fast Recognition

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Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning

Materials ◽

10.3390/ma12101681 ◽

2019 ◽

Vol 12 (10) ◽

pp. 1681 ◽

Cited By ~ 3

Author(s):

Ruofeng Wei ◽

Yunbo Bi

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Defect Detection ◽

Visual Inspection ◽

Surface Defects ◽

Average Precision ◽

Saliency Maps ◽

Aluminum Profile ◽

Safety And Reliability ◽

Aluminum Profiles

Aluminum profile surface defects can greatly affect the performance, safety, and reliability of products. Traditional human-based visual inspection has low accuracy and is time consuming, and machine vision-based methods depend on hand-crafted features that need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect-detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, and 43.3% average precision (AP) for the 10 defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.

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Research on brake pad surface defects detection based on deep learning

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188708 ◽

2020 ◽

Author(s):

Tao Zhang ◽

Yuting Liu ◽

Yaning Yang ◽

Yinggu Jin

Keyword(s):

Deep Learning ◽

Surface Defects ◽

Brake Pad

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Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666201224125653 ◽

2020 ◽

Vol 14 ◽

Author(s):

Dan Luo

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Supervised Learning ◽

Image Recognition ◽

Production Efficiency ◽

Learning Algorithm ◽

Electronic Components ◽

Electron Device ◽

Actual Recognition ◽

The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

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