MID-INFRARED (MIR) OCT-based inspection in industry
- URL: http://arxiv.org/abs/2507.01074v1
- Date: Tue, 01 Jul 2025 11:25:42 GMT
- Title: MID-INFRARED (MIR) OCT-based inspection in industry
- Authors: N. P. García-de-la-Puente, Rocío del Amor, Fernando García-Torres, Niels Møller Israelsen, Coraline Lapre, Christian Rosenberg Petersen, Ole Bang, Dominik Brouczek, Martin Schwentenwein, Kevin Neumann, Niels Benson, Valery Naranjo,
- Abstract summary: This paper aims to evaluate mid-infrared (MIR) Optical Coherence Tomography ( OCT) systems as a tool to penetrate different materials and detect sub-surface irregularities.
- Score: 32.33406552316584
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper aims to evaluate mid-infrared (MIR) Optical Coherence Tomography (OCT) systems as a tool to penetrate different materials and detect sub-surface irregularities. This is useful for monitoring production processes, allowing Non-Destructive Inspection Techniques of great value to the industry. In this exploratory study, several acquisitions are made on composite and ceramics to know the capabilities of the system. In addition, it is assessed which preprocessing and AI-enhanced vision algorithms can be anomaly-detection methodologies capable of detecting abnormal zones in the analyzed objects. Limitations and criteria for the selection of optimal parameters will be discussed, as well as strengths and weaknesses will be highlighted.
Related papers
- Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks [49.84182981950623]
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.<n>It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies.<n>We introduce an efficient and easily deployable multispectral object detection framework that can seamlessly optimize high-performing single-modality models.
arXiv Detail & Related papers (2024-11-27T12:18:39Z) - Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos.
In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
arXiv Detail & Related papers (2024-10-08T00:25:33Z) - A Systematic Review of Available Datasets in Additive Manufacturing [56.684125592242445]
In-situ monitoring incorporating visual and other sensor technologies allows the collection of extensive datasets during the Additive Manufacturing process.
These datasets have potential for determining the quality of the manufactured output and the detection of defects through the use of Machine Learning.
This systematic review investigates the availability of open image-based datasets originating from AM processes that align with a number of pre-defined selection criteria.
arXiv Detail & Related papers (2024-01-27T16:13:32Z) - Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies [2.4541568670428915]
This paper provides an overview of intelligent condition monitoring and fault detection and diagnosis methods for industrial plants.<n>The most popular and state-of-the-art deep learning (DL) and machine learning (ML) algorithms for industrial plant condition monitoring, fault detection, and diagnosis are summarized.<n>A comparison of the accuracies and specifications of different algorithms utilizing the Tennessee Eastman Process (TEP) is conducted.
arXiv Detail & Related papers (2024-01-03T21:35:03Z) - Automated Semiconductor Defect Inspection in Scanning Electron
Microscope Images: a Systematic Review [4.493547775253646]
Machine learning algorithms can be trained to accurately classify and locate defects in semiconductor samples.
Convolutional neural networks have proved to be particularly useful in this regard.
This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images.
arXiv Detail & Related papers (2023-08-16T13:59:43Z) - Spectral Analysis of Marine Debris in Simulated and Observed
Sentinel-2/MSI Images using Unsupervised Classification [0.0]
This study uses Radiative Transfer Model (RTM) simulated data and data from the Multispectral Instrument (MSI) of the Sentinel-2 mission in combination with machine learning algorithms.
The results indicate that the spectral behavior of pollutants is influenced by factors such as the type of polymer and pixel coverage percentage.
These insights can guide future research in remote sensing applications for detecting marine plastic pollution.
arXiv Detail & Related papers (2023-06-26T18:46:47Z) - Deep Industrial Image Anomaly Detection: A Survey [85.44223757234671]
Recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD)
In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques.
We highlight several opening challenges for image anomaly detection.
arXiv Detail & Related papers (2023-01-27T03:18:09Z) - Functional Anomaly Detection: a Benchmark Study [4.444788548423704]
Anomaly detection can now rely on measurements sampled at a very high frequency.
It is the purpose of this paper to investigate the performance of recent techniques for anomaly detection in the functional setup on real datasets.
arXiv Detail & Related papers (2022-01-13T18:20:32Z) - Unsupervised deep learning techniques for powdery mildew recognition
based on multispectral imaging [63.62764375279861]
This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves.
We focus on unsupervised deep learning techniques applied to multispectral imaging data.
We propose the use of autoencoder architectures to investigate two strategies for disease detection.
arXiv Detail & Related papers (2021-12-20T13:29:13Z) - Signal Processing and Machine Learning Techniques for Terahertz Sensing:
An Overview [89.09270073549182]
Terahertz (THz) signal generation and radiation methods are shaping the future of wireless systems.
THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band.
We present an overview of these techniques, with an emphasis on signal pre-processing.
We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band.
arXiv Detail & Related papers (2021-04-09T01:38:34Z) - Real-World Anomaly Detection by using Digital Twin Systems and
Weakly-Supervised Learning [3.0100975935933567]
We present novel weakly-supervised approaches to anomaly detection for industrial settings.
The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery.
The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset.
arXiv Detail & Related papers (2020-11-12T10:15:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.