The defect detection of 3D-printed ceramic curved surface parts with low contrast based on deep learning
Authors:
- Wei ChenWCWei Chen
- Bin ZouBZBin Zou
- Chuanzhen HuangCHChuanzhen Huang
- Jinzhao YangJYJinzhao Yang
- Lei LiLLLei Li
- Jikai LiuJLJikai Liu
- Xinfeng WangXWXinfeng Wang
- 24 September 2022
- journal article
- research article
- Published by Elsevier in Ceramics International
- Vol. 49 (2) , 2881-2893
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (51875319)
- Natural Science Foundation of Shandong Province (ZR2020ZD05)
This publication has 21 references indexed in Scilit:
- Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNNSensors, 2019
- Permeability of porous ceramics by X-ray CT image analysisActa Materialia, 2019
- Segmentation-based deep-learning approach for surface-defect detectionJournal of Intelligent Manufacturing, 2019
- Machine vision intelligence for product defect inspection based on deep learning and Hough transformJournal of Manufacturing Systems, 2019
- An Automated System for Position Monitoring and Correction of Chord-Based Rail Corrugation Measuring PointsIEEE Transactions on Instrumentation and Measurement, 2018
- Evolutionary neuro-fuzzy system for surface roughness evaluationApplied Soft Computing, 2017
- Automatic thresholding for defect detection by background histogram mode extentsJournal of Manufacturing Systems, 2015
- Feature selection for neural network based defect classification of ceramic components using high frequency ultrasoundUltrasonics, 2015
- Application of Halcon in Digital Image and SignalApplied Mechanics and Materials, 2014
- Defocus map estimation from a single imagePattern Recognition, 2011