An effective framework using identification and image reconstruction algorithm for train component defect detection

Zhang, Hao-Dong; Yuan, Xue; Li, Danyong; You, Jane; Zhao, Xiaoming; Cai, Wen-Ming; Ju, Shan Shan

doi:10.1007/s10489-021-02981-4

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…After sintering, dust, oxides, and various types of oxygen content do not meet the standard, causing sedimentation, reducing its strength and causing the product to loosen and fall off. Failure to completely remove impurities and foreign objects can lead to defects in the ceramic body [9][10].…”

Section: Porcelain Product Defect Categorymentioning

confidence: 99%

Dehua porcelain product defect recognition based on image processing

Gao

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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In recent years, with the continuous development of the ceramic industry, the problem of Product defect has become increasingly prominent. Therefore, this article conducted recognition research on Dehua porcelain through image processing technology. Firstly, the background and research significance of this article were elaborated; Secondly, a detailed analysis was conducted on the detection technology and application status of powder and particle size in ceramic processing; Next, the principle and implementation process of defect recognition based on machine vision were elaborated, and experimental results were compared and error corrected. At the same time, improved algorithms were proposed to improve identification accuracy; Finally, this paper tests the performance of the porcelain Product defect recognition model, and the test results show that the resource occupancy rate of this model is between 2% and 4%, and the defect recognition rate is relatively high, which can meet the demand. Compared with the traditional visual inspection, the defect recognition method based on image processing can realize the automatic detection process. It can improve production efficiency and reduce the impact of human errors and subjective judgments. This method can identify the defects of Dehua porcelain products in real time and reduce the time and labor cost of traditional detection methods.

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Section: Porcelain Product Defect Categorymentioning

confidence: 99%