@inproceedings{a5cf691aa7944861b0b2e6462c549624,
title = "An Automatic Recognition Method for PCB Visual Defects",
abstract = "Aiming at the problems of low recognition rate and poor efficiency in traditional manual visual inspection of PCB apparent defects, an effective automatic recognition approach is proposed in this paper. The approach firstly obtains the apparent defect region image based on template matching defect detection algorithm. And then, the features of the apparent defect region image are extracted by combining the gray histogram features and the geometric features. Finally, this paper uses the optimized Support Vector Machine (SVM) classifier for automatic recognition and classification. The simulation results show that the method proposed for PCB's apparent defect in this paper can reach more than 96% recognition rate, which is an effective and feasible automatic recognition method.",
keywords = "SVM, apparent defect, automatic recognition, feature combination",
author = "Zhongqiu Zhang and Xiaodong Wang and Shan Liu and Li Sun and Liye Chen and Yangming Guo",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 ; Conference date: 15-08-2018 Through 17-08-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/SDPC.2018.8664974",
language = "英语",
series = "Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "138--142",
editor = "Chuan Li and Dian Wang and Diego Cabrera and Yong Zhou and Chunlin Zhang",
booktitle = "Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018",
}