Application of improved YOLOv7 based on Swin Transformer in defect detection of 3D printed lattice structures

Yintang Wen; Jiaxing Cheng; Yankai Feng; Mengqi Kang; Yuyan Zhang

doi:10.1117/12.3025552

28 February 2024 Application of improved YOLOv7 based on Swin Transformer in defect detection of 3D printed lattice structures

Yintang Wen, Jiaxing Cheng, Yankai Feng, Mengqi Kang, Yuyan Zhang

Author Affiliations +

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 1307104 (2024) https://doi.org/10.1117/12.3025552
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

This paper utilizes an enhanced YOLOv7 network model, incorporating the Swin Transformer as the backbone network, to enable automated identification of internal defects within 3D printed lattice structures. By harnessing the robust adaptability and contextual capturing capabilities of the Swin Transformer, it effectively mitigates the limitations of YOLOv7 in handling diverse image sizes and detecting small objects. Through validation using CT slice images of the 3D printed lattice structure, the results indicate the recognition accuracy of 96.2%, surpassing the conventional YOLOv7 approach by 1.7%. The effectiveness and superiority of the methods suggested in this study are supported by these findings.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yintang Wen, Jiaxing Cheng, Yankai Feng, Mengqi Kang, and Yuyan Zhang "Application of improved YOLOv7 based on Swin Transformer in defect detection of 3D printed lattice structures", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 1307104 (28 February 2024); https://doi.org/10.1117/12.3025552

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KEYWORDS

Transformers

Defect detection

Education and training

Computed tomography

3D printing

Data modeling

3D modeling

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