@article{ART003048099},
author={Yun Jung Hong and Geon Lee and Jiyoung Woo},
title={Computer Vision-Based Measurement Method for Wire Harness Defect Classification},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={1},
pages={77-84},
doi={10.9708/jksci.2024.29.01.077}
TY - JOUR
AU - Yun Jung Hong
AU - Geon Lee
AU - Jiyoung Woo
TI - Computer Vision-Based Measurement Method for Wire Harness Defect Classification
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 1
PB - The Korean Society Of Computer And Information
SP - 77
EP - 84
SN - 1598-849X
AB - In this paper, we propose a method for accurately and rapidly detecting defects in wire harnesses by utilizing computer vision to calculate six crucial measurement values: the length of crimped terminals, the dimensions (width) of terminal ends, and the width of crimped sections (wire and core portions). We employ Harris corner detection to locate object positions from two types of data. Additionally, we generate reference points for extracting measurement values by utilizing features specific to each measurement area and exploiting the contrast in shading between the background and objects, thus reflecting the slope of each sample. Subsequently, we introduce a method using the Euclidean distance and correction coefficients to predict values, allowing for the prediction of measurements regardless of changes in the wire's position. We achieve high accuracy for each measurement type, 99.1%, 98.7%, 92.6%, 92.5%, 99.9%, and 99.7%, achieving outstanding overall average accuracy of 97% across all measurements.
This inspection method not only addresses the limitations of conventional visual inspections but also yields excellent results with a small amount of data. Moreover, relying solely on image processing, it is expected to be more cost-effective and applicable with less data compared to deep learning methods.
KW - Wire harness;Machine vision;Automation;Process inspection;Image processing
DO - 10.9708/jksci.2024.29.01.077
ER -
Yun Jung Hong, Geon Lee and Jiyoung Woo. (2024). Computer Vision-Based Measurement Method for Wire Harness Defect Classification. Journal of The Korea Society of Computer and Information, 29(1), 77-84.
Yun Jung Hong, Geon Lee and Jiyoung Woo. 2024, "Computer Vision-Based Measurement Method for Wire Harness Defect Classification", Journal of The Korea Society of Computer and Information, vol.29, no.1 pp.77-84. Available from: doi:10.9708/jksci.2024.29.01.077
Yun Jung Hong, Geon Lee, Jiyoung Woo "Computer Vision-Based Measurement Method for Wire Harness Defect Classification" Journal of The Korea Society of Computer and Information 29.1 pp.77-84 (2024) : 77.
Yun Jung Hong, Geon Lee, Jiyoung Woo. Computer Vision-Based Measurement Method for Wire Harness Defect Classification. 2024; 29(1), 77-84. Available from: doi:10.9708/jksci.2024.29.01.077
Yun Jung Hong, Geon Lee and Jiyoung Woo. "Computer Vision-Based Measurement Method for Wire Harness Defect Classification" Journal of The Korea Society of Computer and Information 29, no.1 (2024) : 77-84.doi: 10.9708/jksci.2024.29.01.077
Yun Jung Hong; Geon Lee; Jiyoung Woo. Computer Vision-Based Measurement Method for Wire Harness Defect Classification. Journal of The Korea Society of Computer and Information, 29(1), 77-84. doi: 10.9708/jksci.2024.29.01.077
Yun Jung Hong; Geon Lee; Jiyoung Woo. Computer Vision-Based Measurement Method for Wire Harness Defect Classification. Journal of The Korea Society of Computer and Information. 2024; 29(1) 77-84. doi: 10.9708/jksci.2024.29.01.077
Yun Jung Hong, Geon Lee, Jiyoung Woo. Computer Vision-Based Measurement Method for Wire Harness Defect Classification. 2024; 29(1), 77-84. Available from: doi:10.9708/jksci.2024.29.01.077
Yun Jung Hong, Geon Lee and Jiyoung Woo. "Computer Vision-Based Measurement Method for Wire Harness Defect Classification" Journal of The Korea Society of Computer and Information 29, no.1 (2024) : 77-84.doi: 10.9708/jksci.2024.29.01.077
