@inproceedings{4b7ebe5bd4004f05b3c7177866322272,
title = "Observation of Attention Mechanism Baseline for PCB Surface Inspection System",
abstract = "Printed circuit boards (PCBs) are critical for interconnecting various components and allowing them to communicate with each other. It is critical to ensure that there are no small surface defects that can negatively impact PCB production. Therefore, template matching is often used in PCB surface inspection systems. Despite its popularity, this method can be improved because inspecting a PCB with a template is inefficient. Currently, integrating the surface inspection system with the deep learning method is proving to be more effective in solving this problem. This paper examines three popular deep learning object recognition methods in order to determine which one is the most effective in terms of attention. These three models are called Carafe, Empirical Attention, and ResNeSt. The experimental results showed that ResNeSt with split attention networks achieves the greatest accuracy in deep learning PCB surface inspection system with a mean average precision (mAP) of 99.2% and an average recall (AR) of 99.5%. The result of this study would improve the effectiveness of PCB surface inspection in controlling production lines.",
keywords = "PCB, attention network, deep learning, defect detection",
author = "Fityanul Akhyar and Ledya Novamizanti and Imaddudin, {Muhammad Azka} and {Henda Pratama}, Ikhsanico and Firmansyach, {Shandy Ramanda} and Chang, {Ming Ching} and Lin, {Chih Yang}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2022 ; Conference date: 09-12-2022 Through 10-12-2022",
year = "2022",
doi = "10.1109/APWiMob56856.2022.10014223",
language = "???core.languages.en_GB???",
series = "APWiMob 2022 - Proceedings: 2022 IEEE Asia Pacific Conference on Wireless and Mobile",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "APWiMob 2022 - Proceedings",
}