Multiscale Convolution-Based Probabilistic Classification for Detecting Bare PCB Defects-Reference-Cited by-同舟云学术

Multiscale Convolution-Based Probabilistic Classification for Detecting Bare PCB Defects

Published:2023 Issue: Volume:72 Page:1-8
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Lei Lei¹^ORCID,Li Han-Xiong¹^ORCID,Yang Hai-Dong²^ORCID

Affiliation:

1. Department of Advanced Design and Systems Engineering, City University of Hong Kong, Hong Kong, SAR, China

2. Guangdong Engineering Research Center for Green Manufacturing and Energy Efficiency Optimization, Guangdong University of Technology, Guangzhou, China

Funder

Guangdong-Hong Kong Joint Project

Key Project in Foshan City

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/09989396.pdf?arnumber=9989396

Reference33 articles.

1. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

2. HRIPCB: a challenging dataset for PCB defects detection and classification

3. The Shewhart Control Chart—Tests for Special Causes

4. An Insulator Defect Detection Model in Aerial Images Based on Multiscale Feature Pyramid Network

5. Real-Time Defect Detection of Track Components: Considering Class Imbalance and Subtle Difference Between Classes

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