Online detection of automotive oil pipe surface defects based on improved YOLOv8 algorithm

Zhenghang Yi; Zhenhao Jiang; Junhui Lu

doi:10.1117/12.3038700

22 July 2024 Online detection of automotive oil pipe surface defects based on improved YOLOv8 algorithm

Zhenghang Yi, Zhenhao Jiang, Junhui Lu

Author Affiliations +

Proceedings Volume 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024); 132221B (2024) https://doi.org/10.1117/12.3038700
Event: Third International Conference on Signal Processing and Communication Security (ICSPCS 2024), 2024, Chengdu, China

Abstract

The automotive industry continues to develop, and the auto parts industry chain also needs to continue to upgrade. However, defects on the tubing surface can be a serious safety hazard, so defect detection is essential. Previous methods mainly rely on manual inspection, but due to the variety of surface defects, small area, small diameter and complex texture of tubing, manual inspection efficiency is low, and the accuracy is low. Faced with this challenge, the introduction of automated surface defect detection technology is imperative. This paper analyzes the characteristics and types of defects caused by automotive tubing in actual production and takes YOLOv8 as the baseline model to improve its feature extraction and fusion network part. The specific improvements are as follows: Add deformable convolution module (DCN) to the feature extraction network part of YOLOv8s. Flexibly extend the receptive field of convolution kernel to improve the accuracy of small target detection in low resolution images; The CA attention mechanism was added to the feature fusion network part of YOLOv8s. Strengthen the ability of the network to identify small target defects, so as to improve the accuracy of model detection.

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

Citation Download Citation

Zhenghang Yi, Zhenhao Jiang, and Junhui Lu "Online detection of automotive oil pipe surface defects based on improved YOLOv8 algorithm", Proc. SPIE 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024), 132221B (22 July 2024); https://doi.org/10.1117/12.3038700

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