4.6 Article

Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Instruments & Instrumentation

Introduction of the combination of thermal fundamentals and Deep Learning for the automatic thermographic inspection of thermal bridges and water-related problems in infrastructures

I Garrido et al.

Summary: This paper introduces the combination of an automatic thermogram pre-processing algorithm and a Deep Learning (DL) model, Mask R-CNN, applied to thermal images acquired from different infrastructures, improving the performance of defect detection and introducing DL models to the thermographic inspection of water-related problems and thermal bridges in an infrastructure.

QUANTITATIVE INFRARED THERMOGRAPHY JOURNAL (2023)

Article Materials Science, Characterization & Testing

Automated Defect Detection in Non-planar Objects Using Deep Learning Algorithms

Yuntao Tao et al.

Summary: This paper presents a study on the use of recurrent neural network and artificial feed-forward neural network in pulsed thermography for the automated inspection of non-planar carbon fiber reinforced plastic samples. The quantitative comparison of testing results shows that the long short-term memory recurrent neural network model is more accurate in handling time dependent information compared to the artificial feed-forward neural network model.

JOURNAL OF NONDESTRUCTIVE EVALUATION (2022)

Article Instruments & Instrumentation

Deep convolutional neural networks for classifying breast cancer using infrared thermography

Juan Carlos Torres-Galvan et al.

Summary: This paper proposed the use of a deep convolutional neural network to automatically classify thermograms into normal and abnormal classes, with results showing high sensitivity of the model, validating the effectiveness of using infrared thermography as an adjunct method for breast cancer screening.

QUANTITATIVE INFRARED THERMOGRAPHY JOURNAL (2022)

Article Chemistry, Multidisciplinary

Risk Analysis of Road Tunnels: A Computational Fluid Dynamic Model for Assessing the Effects of Natural Ventilation

Ciro Caliendo et al.

Summary: The study shows that despite exposure to toxic gases and heat during the evacuation process, tunnel users can safely evacuate. The research also suggests that mechanical ventilation may not be necessary in the investigated tunnel, highlighting the importance of natural ventilation effects in tunnels.

APPLIED SCIENCES-BASEL (2021)

Article Geography, Physical

ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

Foivos Diakogiannis et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)

Proceedings Paper Computer Science, Theory & Methods

R-SiamNet: ROI-Align Pooling Baesd Siamese Network for Object Tracking

LiHui Su et al.

THIRD INTERNATIONAL CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2020) (2020)

Article Instruments & Instrumentation

Infrared image sensor developments supported by the European Space Agency

K. Minoglou et al.

INFRARED PHYSICS & TECHNOLOGY (2019)

Article Instruments & Instrumentation

Material classification with laser thermography and machine learning

Tamas Aujeszky et al.

QUANTITATIVE INFRARED THERMOGRAPHY JOURNAL (2019)

Article Materials Science, Characterization & Testing

Temporal and spatial deep learning network for infrared thermal defect detection

Qin Luo et al.

NDT & E INTERNATIONAL (2019)

Article Computer Science, Information Systems

Infrared Thermal Imaging-Based Crack Detection Using Deep Learning

Jun Yang et al.

IEEE ACCESS (2019)

Article Automation & Control Systems

Deep Learning for Infrared Thermal Image Based Machine Health Monitoring

Olivier Janssens et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2018)

Article Materials Science, Multidisciplinary

Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System

Jue Hu et al.

METALS (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Wildland fires detection and segmentation using deep learning

Moulay A. Akhloufi et al.

PATTERN RECOGNITION AND TRACKING XXIX (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Mask R-CNN

Kaiming He et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Survey on Anomaly Detection using Data Mining Techniques

Shikha Agrawal et al.

KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015 (2015)

Article Instruments & Instrumentation

Thermographic Signal Reconstruction with periodic temperature variation applied to moisture classification

Paolo Bison et al.

QUANTITATIVE INFRARED THERMOGRAPHY JOURNAL (2011)

Article Thermodynamics

Analysis of pulsed thermography methods for defect depth prediction

JG Sun

JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME (2006)

Article Computer Science, Artificial Intelligence

Candid covariance-free incremental principal component analysis

JY Weng et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2003)

Article Instruments & Instrumentation

Advances in pulsed phase thermography

X Maldague et al.

INFRARED PHYSICS & TECHNOLOGY (2002)

Article Computer Science, Artificial Intelligence

Classification ability of single hidden layer feedforward neural networks

GB Huang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2000)