Printed Circuit Board Quality Detection Method Integrating Lightweight Network and Dual Attention Mechanism
Author:
Affiliation:
1. College of Mechanical and Electrical Engineering, Shanxi Datong University, Datong, China
2. College of Coal Engineering, Shanxi Datong University, Datong, China
3. College of Business, Shanxi Datong University, Datong, China
Funder
Shanxi Provincial Philosophy and Social Science Planning Project
Shanxi Datong University Scientific Research Yungang Special Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09857914.pdf?arnumber=9857914
Reference23 articles.
1. Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network
2. PCB Defect Detection Using Deep Learning Methods
3. Detecting Defects in PCB using Deep Learning via Convolution Neural Networks
4. Gaussian-IoU loss: Better learning for bounding box regression on PCB component detection
5. CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection
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3. Valid-IoU: an improved IoU-based loss function and its application to detection of defects on printed circuit boards;Multimedia Tools and Applications;2024-06-13
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