Surface Defect Detection of Electronic Connectors Based on Machine Vision
XIA Sixian
GUO Xiaoqiang
ZHANG Xiaoyu
QU Xiangju
HU Zhiqiang
Abstract：This paper presents a defect detection method for electronic connectors based on rotating template matching and local feature recognition.For images of electronic connectors captured on the test bench,preprocessing steps such as smoothing,binarization,and morphological operations are performed to obtain clear target contours.The template matching algorithm is enhanced by incorporating angular rotation in order to preliminarily locate the region containing electronic connectors.To achieve precise detection,a feature vector based on SIFT gradient magnitude and direction is established.A support vector machine model is then trained to classify and detect defects in the target connectors.Experimental validation shows that the proposed electronic connector defect detection algorithm achieves a detection accuracy of 96.3％and a real-time frame rate of 17.4 FPS,outperforming other general deep learning object detection models.This meets the requirements for defect detection in electronic connectors on production assembly lines.
Keywords：machine visiondefect detectioncontour detectionsupport vector machine
Publication Date：2025-12-30
Online Publishing Date：2026-01-17(First online date of this platform, not the publication date of the document)
Pages：8( 85-92 )