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Feature Papers in "Sensing and Imaging" Section 2025&2026

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 12379

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Editors

Prof. Dr. Sylvain Girard

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Guest Editor

Laboratoire Hubert Curien, CNRS UMR 5516, Université de Lyon, 42000 Saint-Étienne, France
Interests: fiber sensors; optical sensors; image sensors; optical materials; radiation effects
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Christoph M. Friedrich

E-Mail Website
Guest Editor

1. Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), 44227 Dortmund, Germany
2. Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, 45122 Essen, Germany
Interests: machine learning; computational intelligence; biomedical applications; interpretable machine learning; natural language processing (NLP); computer vision; augmented reality; information extraction; information retrieval; image processing; biostatistics; bioinformatics; mathematics for computer science
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Prof. Dr. Bogdan Smolka

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Guest Editor

Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: image denoising; image segmentation; image super-resolution; object detection; deep learning-based filtering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Sensors Section ‘Sensing and Imaging’ is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions and recommendations from the EBMs.

We are seeking original papers and review articles that showcase state-of-the-art theoretical and applicative advances, new experimental discoveries, and novel technological improvements regarding sensing and imaging. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be well promoted.

We would also like to take this opportunity to call on more experienced scholars to join the Section ‘Sensing and Imaging’ so that we can work together to further develop this exciting field of research.

Prof. Dr. Sylvain Girard
Prof. Dr. Christoph M. Friedrich
Prof. Dr. Bogdan Smolka
Guest Editors

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

imaging systems
sensors
camera
radar and sonar
probes
diagnostics (bio)medical imaging
subsurface and surface sensing
environmental sensing and imaging
nondestructive sensing and imaging
aerial/UAV imaging
gesture/pattern/target recognition
computer vision
imaging in harsh environments
extended reality (mixed/augmented/virtual reality)
material appearance metrology
acoustic/ultrasound/photoacoustic imaging
optical/infrared/spectral/hyperspectral/fluorescence imaging
(micro)wave/terahertz imaging
thermal imaging
magnetic imaging/atomic scale magnetic sensing and imaging
(high dynamic) range imaging
2D/3D imaging
computational imaging
unconventional imaging
artificial intelligence (AI)
machine/deep learning
(convolutional) neutral network

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Published Papers (7 papers)

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Research

24 pages, 8414 KB

Open AccessArticle

An Improved YOLOv9 Based Object Detection with Attention Mechanism for Personal Protective Equipment

by Geunho Lee, Jieun Lee, Tae-yong Kim and Jongpil Jeong

Sensors 2026, 26(10), 3058; https://doi.org/10.3390/s26103058 - 12 May 2026

Viewed by 940

Abstract

Industrial sites pose numerous hazards where unexpected accidents can occur at any time, and personal protective equipment (PPE) is a primary safeguard for worker safety. In this study, PPE specifically refers to safety helmets, safety shoes, and safety gloves. Manual verification of PPE usage is infeasible in environments with many workers, motivating automated detection. This study proposes a method that integrates the Convolutional Block Attention Module (CBAM) exclusively into the training-only auxiliary reversible branch of YOLOv9’s Programmable Gradient Information (PGI) architecture. The proposed CBAMLinear module enhances gradient information during training while introducing zero additional computational overhead at inference, as the entire auxiliary branch is removed. The proposed YOLOv9 with CBAMLinear achieved consistent mAP@0.5:0.95 gains of 0.005–0.007 over the baseline for the three larger model variants, while maintaining identical inference-time parameters and FLOPs. In industrial safety, even modest performance gains can directly contribute to accident prevention by reducing false positives and false negatives, making this approach well suited for real-time safety management systems in industrial settings. Full article

(This article belongs to the Special Issue Feature Papers in "Sensing and Imaging" Section 2025&2026)

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17 pages, 6883 KB

Open AccessArticle

A Comparative Evaluation of Super-Resolution Methods for Spectral Images Using Pretrained RGB Models

by Navid Shokoohi, Abdelhamid N. Fsian, Jean-Baptiste Thomas and Pierre Gouton

Sensors 2026, 26(2), 683; https://doi.org/10.3390/s26020683 - 20 Jan 2026

Viewed by 1160

Abstract

The spatial resolution of spectral imaging systems is fundamentally constrained by hardware trade-offs, and the availability of large-scale annotated spectral datasets remains limited. This study presents a comprehensive evaluation of super-resolution (SR) methods across interpolation-based, CNN-based, GAN-based, and diffusion-based approaches. Using a synthetic 30-band spectral representation reconstructed from RGB with the MST++ model as a proxy ground truth, we arrange non-adjacent triplets as three-channel PNG inputs to ensure compatibility with existing SR architectures. A unified pipeline enables reproducible evaluation at ×2, ×4, and ×8 scales on 50 unseen images, with performance assessed using PSNR, SSIM, and SAM. Results confirm that bicubic interpolation remains a spectrally reliable baseline; shallow CNNs (SRCNN, FSRCNN) generalize well without fine-tuning; and ESRGAN improves spatial detail at the expense of spectral accuracy. Diffusion models (SR3, ResShift, SinSR), evaluated in a zero-shot setting without spectral-domain adaptation, exhibit unstable performance and require spectrum-aware training to preserve spectral structure effectively. The findings underscore a persistent trade-off between perceptual sharpness and spectral fidelity, highlighting the importance of domain-aware objectives when applying generative SR models to spectral data. This work provides reproducible baselines and a flexible evaluation framework to support future research in spectral image restoration. Full article

(This article belongs to the Special Issue Feature Papers in "Sensing and Imaging" Section 2025&2026)

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22 pages, 18817 KB

Open AccessArticle

Integration of X-Ray CT, Sensor Fusion, and Machine Learning for Advanced Modeling of Preharvest Apple Growth Dynamics

by Weiqun Wang, Dario Mengoli, Shangpeng Sun and Luigi Manfrini

Sensors 2026, 26(2), 623; https://doi.org/10.3390/s26020623 - 16 Jan 2026

Viewed by 728

Abstract

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R² > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability. Full article

(This article belongs to the Special Issue Feature Papers in "Sensing and Imaging" Section 2025&2026)

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30 pages, 8197 KB

Open AccessArticle

Numerical and Experimental Study of Mode Coupling Due to Localised Few-Mode Fibre Bragg Gratings and a Spatial Mode Multiplexer

by James Hainsworth, Adriana Morana, Lucas Lescure, Philippe Veyssiere, Sylvain Girard and Emmanuel Marin

Sensors 2025, 25(19), 6087; https://doi.org/10.3390/s25196087 - 2 Oct 2025

Viewed by 1538

Abstract

Mode conversion effects in Fibre Bragg Gratings (FBGs) are widely exploited in applications such as sensing and fibre lasers. However, when FBGs are inscribed into Few-mode optical Fibres (FMFs), the mode interactions become highly complex due to the increased number of guided modes, rendering their practical use difficult. In this study, we investigate whether the addition of a spatial mode multiplexer, used to selectively excite specific fibre modes, can simplify the interpretation and utility of few-mode FBGs (FM-FBGs). We focus on point-by-point (PbP)-inscribed FBGs, localised with respect to the transverse cross-section of the fibre core, and study their interaction with a range of Hermitian Gauss input modes. We present a comprehensive numerical study supported by experimental validation, examining the mechanisms of mode coupling induced by localised FBGs and its implications, with a focus on sensing applications. Our results show that the introduction of a spatial mode multiplexer leads to slight simplification of the FBG transmission spectrum. Nevertheless, significant simplification of the reflection spectrum is achievable after modal filtering occurs as the reflected light re-traverses the spatial mode multiplexer, potentially enabling WDM monitoring of FM-FBGs. Notably, we report a novel approach to multiplexing FBGs based on their transverse location within the fibre core and the modal content initially coupled into the fibre. To the best of our knowledge, this multiplexing technique is yet to be reported. Full article

(This article belongs to the Special Issue Feature Papers in "Sensing and Imaging" Section 2025&2026)

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11 pages, 4686 KB

Open AccessArticle

Authentication of Counterfeit Electronics Using Rapid THz Time-of-Flight Imaging

by Hyeonseung Ryu, Byung Hee Son, Jihwan Kim, Jangsun Kim and Yeong Hwan Ahn

Sensors 2025, 25(16), 5160; https://doi.org/10.3390/s25165160 - 19 Aug 2025

Cited by 2 | Viewed by 1569

Abstract

In this study, we used rapid terahertz (THz) time-of-flight (ToF) imaging to identify counterfeit electronics prevalent in the online market. THz-ToF allows the nondestructive reflection imaging of portable electronic devices enclosed by plastic cases with a spatial resolution of 1 mm and a depth range larger than 5 mm. For instance, we could identify a counterfeit solid-state device because there was a striking difference in the internal structure compared with that of an authentic device, although its appearance was similar. It is also possible to authenticate small portable devices with arbitrary shapes, such as earphones; THz-ToF imaging can be applied to authentication from a variety of perspectives. Importantly, our technique is particularly useful for identifying the inappropriate use of integrated circuit (IC) chips, such as controller chips in USB hub devices. THz-TDS images for various portable and wearable devices will be useful for rapid in-line authentication of products and will offer an effective tool for identifying fake electronics and the inappropriate use of IC chips in various equipment. Full article

(This article belongs to the Special Issue Feature Papers in "Sensing and Imaging" Section 2025&2026)

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11 pages, 2547 KB

Open AccessArticle

Simultaneous Remote Non-Invasive Blood Glucose and Lactate Measurements by Mid-Infrared Passive Spectroscopic Imaging

by Ruka Kobashi, Daichi Anabuki, Hibiki Yano, Yuto Mukaihara, Akira Nishiyama, Kenji Wada, Akiko Nishimura and Ichiro Ishimaru

Sensors 2025, 25(15), 4537; https://doi.org/10.3390/s25154537 - 22 Jul 2025

Cited by 1 | Viewed by 2454

Abstract

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article

(This article belongs to the Special Issue Feature Papers in "Sensing and Imaging" Section 2025&2026)

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42 pages, 47882 KB

Open AccessArticle

Product Engagement Detection Using Multi-Camera 3D Skeleton Reconstruction and Gaze Estimation

by Matus Tanonwong, Yu Zhu, Naoya Chiba and Koichi Hashimoto

Sensors 2025, 25(10), 3031; https://doi.org/10.3390/s25103031 - 11 May 2025

Cited by 1 | Viewed by 2547

Abstract

Product engagement detection in retail environments is critical for understanding customer preferences through nonverbal cues such as gaze and hand movements. This study presents a system leveraging a 360-degree top-view fisheye camera combined with two perspective cameras, the only sensors required for deployment, effectively capturing subtle interactions even under occlusion or distant camera setups. Unlike conventional image-based gaze estimation methods that are sensitive to background variations and require capturing a person’s full appearance, raising privacy concerns, our approach utilizes a novel Transformer-based encoder operating directly on 3D skeletal keypoints. This innovation significantly reduces privacy risks by avoiding personal appearance data and benefits from ongoing advancements in accurate skeleton estimation techniques. Experimental evaluation in a simulated retail environment demonstrates that our method effectively identifies critical gaze-object and hand-object interactions, reliably detecting customer engagement prior to product selection. Despite yielding slightly higher mean angular errors in gaze estimation compared to a recent image-based method, the Transformer-based model achieves comparable performance in gaze-object detection. Its robustness, generalizability, and inherent privacy preservation make it particularly suitable for deployment in practical retail scenarios such as convenience stores, supermarkets, and shopping malls, highlighting its superiority in real-world applicability. Full article

(This article belongs to the Special Issue Feature Papers in "Sensing and Imaging" Section 2025&2026)

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