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An industrial defect detection algorithm based on CPU-GPU parallel call

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Abstract

The workpiece positioning and defect segmentation are two key steps in the workpiece detection process. This paper has designed a CPU-GPU parallel call algorithm based on real industrial quality inspection conditions to realize the high-speed workpiece defect detection. The algorithm can fully utilize all computing resources of the industrial control computer while simultaneously accomplishing the workpiece positioning and defect segmentation tasks. Moreover, to reduce the workpiece defect detection's scope and improve the defect segmentation algorithm's efficiency, the proposed method uses the workpiece positioning results. As for the positioning task, we have designed the double pyramid method to enhance the positioning speed. When it comes to the defect segmentation task, we have introduced the lightweight network to improve the workpiece segmentation speed. Considering that the current general data sets are of the workpiece local image(s) post cutting, we set up a new data set to reflect the situation in the industrial field. It consists of images taken from real industrial fields that can better verify the whole quality inspection algorithm process, including the positioning and segmentation algorithms. According to our experiment, our algorithm accomplished the positioning and defect segmentation tasks at a speed of 116FPS. Additionally, the segmentation accuracy reached 75.12% Mean IoU.

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Funding

This work was supported by the Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou, China.

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Authors and Affiliations

  1. School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310000, China

    Zhu Li, Hong-wei Lin, Yuan-yuan Liu, Chong Chen & Yun-fei Xia

Authors
  1. Zhu Li
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  2. Hong-wei Lin
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  3. Yuan-yuan Liu
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  4. Chong Chen
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  5. Yun-fei Xia
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Contributions

Conceptualization: Z.L. and H.L.

Formal analysis: Z.L. and H.L.

Investigation: H.L., Z.L. and Y.L..

Methodology: Z.L. and C.C.

Project administration: Z.L.

Resources: H.L.

Validation: H.L.

Writing—original draft: H.L. and Z.L.

Writing—review & editing: C.C.,Y.X. and H.L.

Corresponding author

Correspondence to Hong-wei Lin.

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Li, Z., Lin, Hw., Liu, Yy. et al. An industrial defect detection algorithm based on CPU-GPU parallel call. Multimed Tools Appl 82, 44191–44207 (2023). https://doi.org/10.1007/s11042-023-15613-5

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  • DOI: https://doi.org/10.1007/s11042-023-15613-5

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