Stainless steel welded pipe weld seam defect detection method based on improved YOLOv5s

Yukun Sun; Huaishu Hou

doi:10.1117/12.3048330

3 October 2024 Stainless steel welded pipe weld seam defect detection method based on improved YOLOv5s

Yukun Sun, Huaishu Hou

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132722G (2024) https://doi.org/10.1117/12.3048330
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Aiming at the problem of low efficiency and low accuracy of stainless steel welded pipe weld surface defect detection in industrial scenarios, a welded pipe defect detection algorithm based on the improved YOLOv5s is proposed. First, the C3 module in YOLOv5s is replaced with the C2f module, which has fewer parameters and is more sensitive to small targets, to obtain richer gradient information and enhance the feature extraction capability. Secondly, the SimAM parameter-free attention mechanism is introduced to strengthen the model's simultaneous attention to channel and spatial information, which gives higher priority to defective features without increasing any number of parameters, improves the accuracy of the model, and improves the detection performance of the model in various scenarios. The experimental findings demonstrate that the improved algorithm, compared with the traditional YOLOv5s algorithm, improves the mAP by 4.4%,the frame rate FPS reaches 140F/s, and the number of parameters is reduced by 18%; it is easier to be deployed on the inspection equipment in the production environment while meeting the need for rapid and precise detection. The approach offers robust support for detecting defects in stainless steel welded pipe, and provides a reference for inspection intelligence in on-line production of welded pipe.

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

Citation Download Citation

Yukun Sun and Huaishu Hou "Stainless steel welded pipe weld seam defect detection method based on improved YOLOv5s", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132722G (3 October 2024); https://doi.org/10.1117/12.3048330

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