An improved YOLOv7 algorithm for laser welding defect detection

Li Zhang; Yunjie He; Yunhao Zhou; Yanfeng Chen; Ziliang Chen; Yatao Yang

doi:10.1117/12.3003811

11 October 2023 An improved YOLOv7 algorithm for laser welding defect detection

Li Zhang, Yunjie He, Yunhao Zhou, Yanfeng Chen, Ziliang Chen, Yatao Yang

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001H (2023) https://doi.org/10.1117/12.3003811
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Recently, with the development of new energy vehicles, there has been an increasing demand for new energy batteries. During the production process of batteries, laser welding is an important technology, which can produce defects inevitably. So, the defects detection of laser welding is absolutely necessary to ensure the quality of the batteries. In this paper, we proposed an improved YOLOv7 algorithm for laser welding defect detection of battery pole. The algorithm adopts Conv2former to improve the model's capability to extract contextual information and introduces the CBAM attention mechanism to extract key information. The loss function is also modified to WIoU to further improve the accuracy of the model. The experiment results on the battery pole dataset show that the improved YOLOv7 achieved a 3.1% increase in map@0.5, reaching 0.921, and a 2.1% increase in map@0.5:0.95, reaching 0.682 compared to the original YOLOv7.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Zhang, Yunjie He, Yunhao Zhou, Yanfeng Chen, Ziliang Chen, and Yatao Yang "An improved YOLOv7 algorithm for laser welding defect detection", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001H (11 October 2023); https://doi.org/10.1117/12.3003811

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available