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Advanced Sensing Technologies in Industrial Defect Detection
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Advanced Sensing Technologies in Industrial Defect Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3477

Special Issue Editor

Dr. Qiwu Luo

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: computer vision; molecular spectroscopy; industrial automatic optic inspection; industrial defect detection; parallel hardware architecture design; reconfigurable computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced sensing technologies are pivotal in driving the evolution of the global industrial manufacturing equipment sector. As a core enabler for improving manufacturing quality and ensuring product reliability, these technologies deliver essential data feedback for quality inspection and operational control throughout industrial processes. Nevertheless, the deployment of sophisticated sensing systems in industrial defect detection continues to encounter significant challenges. Real-world production settings are often characterized by multiple sources of interference, complex backgrounds, and stringent real-time requirements, all of which can hinder the effective acquisition and timely analysis of critical information. As a result, developing robust, precise, and real-time sensing solutions capable of performing reliably under demanding industrial conditions has emerged as a key focus of cutting-edge research in both academic and industrial communities.

This Special Issue invites contributions on intelligent sensing and monitoring methodologies tailored for industrial applications. It aims to present state-of-the-art research addressing the challenges and opportunities in industrial defect detection through innovative sensing approaches. We welcome original studies on novel sensing technologies, industrial image processing algorithms, optical inspection techniques, and practical implementation experiences. Topics of interest include, but are not limited to, the following: real-time industrial image processing, precision optical measurement technologies, sensing technologies in complex industrial environments, and industrial defect recognition in intricate industrial scenarios. Critical reviews and surveys of the cutting-edge and practice are also encouraged.

Dr. Qiwu Luo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spectral analysis technology
  • industrial automated optical inspection
  • industrial defect detection
  • advanced industrial sensing technology
  • time series signal processing
  • optical sensing and measurement
  • automated visual inspection
  • laser measurement technology

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Published Papers (5 papers)

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Research

17 pages, 2477 KB  
Article
MHA-PINN: A Novel Physics-Informed Neural Network for Predicting Fiber Dyeability
by Feier Zhou, Yuxiang Liu, Shuo Yang, Fan Guo, Xiaofeng Yuan and Ruimin Xie
Sensors 2026, 26(7), 2018; https://doi.org/10.3390/s26072018 - 24 Mar 2026
Viewed by 532
Abstract
Fiber dyeability is a core indicator of textile quality and added value. Pre-experiment accurate prediction of fiber dyeability reduces the waste and inefficiency of trial-and-error methods. However, due to the limited data volume and complex mechanisms of fiber dyeability, there are no relevant [...] Read more.
Fiber dyeability is a core indicator of textile quality and added value. Pre-experiment accurate prediction of fiber dyeability reduces the waste and inefficiency of trial-and-error methods. However, due to the limited data volume and complex mechanisms of fiber dyeability, there are no relevant studies to date. Thus, this paper proposes a novel prediction model integrating domain knowledge and process data called multi-head attention–physics-informed neural network (MHA-PINN). Within the MHA-PINN framework, limited experimental data is first augmented by using variational autoencoders, and subjected to ensemble feature selection on the augmented samples. Subsequently, a multi-head attention layer is introduced to capture the interdependencies among sample variables, thereby outputting a new feature matrix that represents the weighted fusion of these variables. Finally, a physics-informed neural network module embeds the dyeing kinetic equations into the loss function, guiding the model to converge towards accurate solutions for sample predictions. The effectiveness and superiority of the proposed MHA-PINN have been validated on a fiber dyeability experimental dataset. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Industrial Defect Detection)
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26 pages, 10734 KB  
Article
A Residual Amplitude Modulation Noise Suppression Method Based on Multi-Harmonic Component Decoupling
by Qiwu Luo, Hang Su, Yibo Wang and Chunhua Yang
Sensors 2026, 26(6), 1841; https://doi.org/10.3390/s26061841 - 14 Mar 2026
Viewed by 432
Abstract
Wavelength modulation spectroscopy (WMS) is a representative implementation of tunable diode laser absorption spectroscopy (TDLAS), enabling reliable gas component analysis with concentration-related information derived from harmonic component extraction, while offering enhanced noise immunity for trace gas sensing in open environments. However, due to [...] Read more.
Wavelength modulation spectroscopy (WMS) is a representative implementation of tunable diode laser absorption spectroscopy (TDLAS), enabling reliable gas component analysis with concentration-related information derived from harmonic component extraction, while offering enhanced noise immunity for trace gas sensing in open environments. However, due to the strong coupling between laser wavelength and intensity, wavelength modulation inevitably introduces residual amplitude modulation (RAM), which significantly degrades measurement accuracy. To address this issue, this study introduces a RAM suppression algorithm based on multiple harmonic component decoupling (MHCD), using the second-harmonic lateral peak inclination angle (LPIA) as a characteristic indicator. Unit harmonic operators for the first, second, and third harmonics are designed, and an original harmonic reconstruction model is established via linear superposition of harmonic components. The optimal harmonic component ratio is determined at the composite operator with the maximum cross-correlation coefficient, and RAM noise is eliminated through a multi-harmonic decoupling matrix. Repetitive measurements on 22 mm pharmaceutical vials with 4% oxygen concentration demonstrate that MHCD reduces the second-harmonic LPIA from 18.07° to 8.56°. Concentration discrimination experiments conducted on seven groups of 22 mm vials with 2% concentration steps (0–12%) show that MHCD increases the true positive rate by 6–11% and decreases the false positive rate by 4–9%, confirming its effectiveness for pharmaceutical online inspection applications. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Industrial Defect Detection)
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17 pages, 1851 KB  
Article
Spatio-Temporal Graph Neural Networks for Anomaly Detection in Complex Industrial Processes
by Shutian Zhao, Hang Zhang, Bei Sun and Yijun Wang
Sensors 2026, 26(5), 1597; https://doi.org/10.3390/s26051597 - 4 Mar 2026
Viewed by 913
Abstract
With the advancement of intelligent manufacturing strategies, Cyber–Physical Production Systems (CPPSs) generate massive amounts of multidimensional, dynamic, and non-stationary data, posing significant challenges to real-time Process Monitoring. Existing anomaly detection methods often suffer from insufficient feature robustness when dealing with complex spatio-temporal dynamics, [...] Read more.
With the advancement of intelligent manufacturing strategies, Cyber–Physical Production Systems (CPPSs) generate massive amounts of multidimensional, dynamic, and non-stationary data, posing significant challenges to real-time Process Monitoring. Existing anomaly detection methods often suffer from insufficient feature robustness when dealing with complex spatio-temporal dynamics, high computational complexity, and difficulties in effectively capturing incipient faults within deep topological structures. To address these issues, this paper proposes a Spatio-Temporal Variational Graph Statistical Attention Autoencoder (ST-VGSAE). First, the framework performs end-to-end multi-scale temporal decomposition via an Adaptive Lifting Wavelet Module, which enhances feature robustness while effectively suppressing noise. Furthermore, a spatio-temporal Token statistical self-attention mechanism with linear complexity is incorporated. By modulating local features via global statistics, it significantly reduces computational costs while enhancing anomaly discriminability. Experiments on the Tennessee Eastman (TE) process dataset demonstrate that the proposed model significantly outperforms state-of-the-art methods in key metrics such as the Fault Detection Rate and the False Alarm Rate, exhibiting superior noise robustness and real-time performance. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Industrial Defect Detection)
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27 pages, 70264 KB  
Article
TaDP-Det: Semi-Supervised Texture-Aware Dynamic Pseudo-Labeling Detector for Industrial Surface Defect Detection
by Qiwu Luo, Weiyu Zhan and Jiaojiao Su
Sensors 2026, 26(4), 1085; https://doi.org/10.3390/s26041085 - 7 Feb 2026
Viewed by 586
Abstract
Surface defect detection is essential for industrial quality control, but obtaining reliable labeled data remains costly due to the need for expert annotation. Semi-supervised object detection (SSOD) mitigates this need by leveraging unlabeled data through pseudo-labeling. However, industrial surface imagery presents specific challenges, [...] Read more.
Surface defect detection is essential for industrial quality control, but obtaining reliable labeled data remains costly due to the need for expert annotation. Semi-supervised object detection (SSOD) mitigates this need by leveraging unlabeled data through pseudo-labeling. However, industrial surface imagery presents specific challenges, including texture-ambiguous, low-contrast backgrounds that cause foreground–background confusion and strong class-dependent detection difficulty, which renders global confidence thresholds ineffective, often yielding noisy and imbalanced pseudo labels. To overcome these limitations, we propose TaDP-Det, a semi-supervised detector that improves pseudo-label quality through dual enhancements in feature representation and label filtering. We first introduce a Texture Enhance Module (TEM), designed as a texture-aware patch-level mixture-of-experts applied at shallow backbone stages, which amplifies discriminative low-level texture cues to generate more reliable pseudo labels in ambiguous regions. Second, the class-wise dynamic pseudo-label filtering (CDPF) scheme uses lightweight 1D Gaussian mixture models to adaptively determine per-class thresholds, preserving challenging defects and suppressing spurious predictions. Comprehensive evaluations on the NEU-DET, GC10-DET, and PCB-DEFECT datasets show that TaDP-Det consistently outperforms state-of-the-art SSOD baselines in mean average precision (mAP) with only modest computational overhead. The results underscore the effectiveness of our method for robust semi-supervised defect detection in industrial applications. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Industrial Defect Detection)
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26 pages, 4671 KB  
Article
MobileSteelNet: A Lightweight Steel Surface Defect Classification Network with Cross-Interactive Efficient Multi-Scale Attention
by Xiang Zou, Zhongming Liu, Chengjun Xu, Jiawei Zhang and Zhaoyu Li
Sensors 2026, 26(3), 1022; https://doi.org/10.3390/s26031022 - 4 Feb 2026
Viewed by 489
Abstract
Steel surface defect classification is critical for industrial quality control, yet existing methods struggle to balance accuracy and efficiency for real-time deployment in vision-based sensor systems. This paper presents MobileSteelNet, a lightweight deep learning framework that introduces two novel modules: multi-scale feature fusion [...] Read more.
Steel surface defect classification is critical for industrial quality control, yet existing methods struggle to balance accuracy and efficiency for real-time deployment in vision-based sensor systems. This paper presents MobileSteelNet, a lightweight deep learning framework that introduces two novel modules: multi-scale feature fusion (MSFF), for integrating multi-stage features; and Cross-Interactive Efficient Multi-Scale Attention (CIEMA), which unifies inter-channel interaction, parallel multi-scale spatial extraction, and grouped efficient computation. Experiments on the NEU-DET dataset demonstrate that MobileSteelNet achieves 91.36% average accuracy, surpassing ResNet-50 (88.01%) and lightweight networks, including MobileNetV2 (86.08%). Notably, it achieves 93.70% accuracy on Scratch-type defects, representing an 82.12 percentage point improvement over baseline MobileNetV1. With a model size of only 8.2 MB, MobileSteelNet maintains superior performance while meeting lightweight deployment requirements, making it suitable for edge deployment in vision sensor systems for steel manufacturing. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Industrial Defect Detection)
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