文章詳目資料
Journal of Computers EIMEDLINEScopus
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| 篇名 | Computer Vision Recognition Method for Surface Defects of Casting Workpieces |
|---|---|
| 卷期 | 34:3 |
| 作者 | Xiaoning Bo 、 Jin Wang 、 Qingfang Liu 、 Peng Yang 、 Honglan Li |
| 頁次 | 305-313 |
| 關鍵字 | casting defects 、 deep learning 、 computer visio 、 EI 、 MEDLINE 、 Scopus |
| 出刊日期 | 202306 |
| DOI | 10.53106/199115992023063403022 |
中文摘要
英文摘要
To improve the recognition efficiency of surface defects in castings, this article first uses median filtering algorithm to denoise the defect image to distinguish between defects and background. Then, gray threshold method is used to segment the image, and the processed image is sent to the improved RefineDet network structure. Improving the RefineDet network structure mainly improves the network depth and incorporates dataset augmentation algorithms. Finally, an experimental platform was built to train, recognize, and compare the collected image dataset. The results show that the accuracy of detecting porosity, blowhole, and flaw defects is 95.6% and 97.3% and 98.15%, the method proposed in this article is accurate and efficient.
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