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Sensors and Advanced Sensing Techniques for Computer Vision Applications: Second Edition

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

Deadline for manuscript submissions: 25 February 2026 | Viewed by 4191

Special Issue Editors

Prof. Dr. Christos Nikolaos E. Anagnostopoulos

E-Mail Website
Guest Editor
Cultural Technology and Communication Department, University of the Aegean, Mytilene, Greece
Interests: 3D modeling; 3D visualization; digital culture; face recognition; mixed reality; multimedia
Special Issues, Collections and Topics in MDPI journals
Dr. Stelios Krinidis

E-Mail Website
Guest Editor
Department of Management Science & Technology, Democritus University of Thrace (DUTH), Agios Loukas, 65404 Kavala, Greece
Interests: signal processing (one-dimensional, image, video, 3D, 4D, multidimensional); computational vision; artificial intelligence; information analysis; data mining; big data analysis; decision-making system; optimization methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on “Sensors and Advanced Sensing Techniques for Computer Vision Applications: Second Edition” addresses all topics related to the challenging problems of computer vision and pattern recognition within the emerging field of deep learning. Technologies related to computational intelligence, including deep learning, neural networks, and soft computing, will be considered from both theoretical and technological points of view, with the use of advanced 2D/3D computer vision and visualization infrastructures.

Classical computer vision systems use visible-light 2D cameras, creating a 3D scene using photogrammetry, while 3D vision systems use more sophisticated acquisition sensors, such as structured-light 3D scanners, thermographic cameras, hyperspectral imagers, and lidar scanners. As a consequence, the classical tasks of computer vision are now handled in 3D space (point clouds, meshes, 3D objects), and artificial intelligence has found a new area to thrive as deep learning is used for the comparative analysis of huge amounts of data.

The topics of interest for this Special Issue include (but are not limited to) the following aspects of computer vision and pattern recognition:

  • Deep learning for 2D/3D object recognition and classification;
  • Reinforcement learning and robotic agents;
  • Data augmentation in computer vision;
  • Digital twins;
  • The multidisciplinary applications of deep learning, pattern recognition, and computer vision.

Prof. Dr. Christos Nikolaos E. Anagnostopoulos
Dr. Stelios Krinidis
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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

  • computer vision
  • pattern recognition
  • digital twins
  • 2D/3D object recognition
  • 3D scanners cameras

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Published Papers (5 papers)

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Research

15 pages, 2201 KB  
Article
CGFusionFormer: Exploring Compact Spatial Representation for Robust 3D Human Pose Estimation with Low Computation Complexity
by Tao Lu, Hongtao Wang and Degui Xiao
Sensors 2025, 25(19), 6052; https://doi.org/10.3390/s25196052 - 1 Oct 2025
Viewed by 384
Abstract
Transformer-based 2D-to-3D lifting methods have demonstrated outstanding performance in 3D human pose estimation from 2D pose sequences. However, they still encounter challenges with the relatively poor quality of 2D joints and substantial computational costs. In this paper, we propose a CGFusionFormer to address [...] Read more.
Transformer-based 2D-to-3D lifting methods have demonstrated outstanding performance in 3D human pose estimation from 2D pose sequences. However, they still encounter challenges with the relatively poor quality of 2D joints and substantial computational costs. In this paper, we propose a CGFusionFormer to address these problems. We propose a compact spatial representation (CSR) to robustly generate local spatial multihypothesis features from part of the 2D pose sequence. Specifically, CSR models spatial constraints based on body parts and incorporates 2D Gaussian filters and nonparametric reduction to improve spatial features against low-quality 2D poses and reduce the computational cost of subsequent temporal encoding. We design a residual-based Hybrid Adaptive Fusion module that combines multihypothesis features with global frequency domain features to accurately estimate the 3D human pose with minimal computational cost. We realize CGFusionFormer with a PoseFormer-like transformer backbone. Extensive experiments on the challenging Human3.6M and MPI-INF-3DHP benchmarks show that our method outperforms prior transformer-based variants in short receptive fields and achieves a superior accuracy–efficiency trade-off. On Human3.6M (sequence length 27, 3 input frames), it achieves 47.6 mm Mean Per Joint Position Error (MPJPE) at only 71.3 MFLOPs, representing about a 40 percent reduction in computation compared with PoseFormerV2 while attaining better accuracy. On MPI-INF-3DHP (81-frame sequences), it reaches 97.9 Percentage of Correct Keypoints (PCK), 78.5 Area Under the Curve (AUC), and 27.2 mm MPJPE, matching the best PCK and achieving the lowest MPJPE among the compared methods under the same setting. Full article
(This article belongs to the Special Issue Sensors and Advanced Sensing Techniques for Computer Vision Applications: Second Edition)
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20 pages, 6846 KB  
Article
Analysis of AWS Rekognition and Azure Custom Vision Performance in Parking Sign Recognition
by Maria Spichkova, Amanda Severin, Chanakan Amornpatchara, Fiona Le, Thuc Hi Tran and Prathiksha Padmaprasad
Sensors 2025, 25(19), 5983; https://doi.org/10.3390/s25195983 - 26 Sep 2025
Viewed by 474
Abstract
Automated recognition and analysis of parking signs can greatly enhance the safety and efficiency of both autonomous vehicles and drivers seeking navigational assistance. Our study focused on identifying parking constraints from the parking signs. It offers the following novel contributions: (1) A comparative [...] Read more.
Automated recognition and analysis of parking signs can greatly enhance the safety and efficiency of both autonomous vehicles and drivers seeking navigational assistance. Our study focused on identifying parking constraints from the parking signs. It offers the following novel contributions: (1) A comparative performance analysis of AWS Rekognition and Azure Custom Vision (CV), two leading services for image recognition and analysis. (2) The first AI-based approach to recognising parking signs typical for Melbourne, Australia, and extracting parking constraint information from them. We utilised 1225 images of the parking signs to evaluate the AI capabilities for analysing these constraints. Both platforms were assessed based on several criteria, including their accuracy in recognising elements of parking signs, sub-signs, and the completeness of the signs. Our experiments demonstrated that both platforms performed effectively and are close to being ready for live application on parking sign analysis. AWS Rekognition demonstrated better results for recognition of parking sign elements and sub-signs (F1 scores of 0.991 and 1.000). It also performed better in the criterion “No text missed”, providing the result of 0.94. Azure CV performed better in the recognition of arrows (F1 score of 0.941). Both approaches demonstrated a similar level of performance for other criteria. Full article
(This article belongs to the Special Issue Sensors and Advanced Sensing Techniques for Computer Vision Applications: Second Edition)
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15 pages, 1786 KB  
Article
Application of Gaussian SVM Flame Detection Model Based on Color and Gradient Features in Engine Test Plume Images
by Song Yan, Yushan Gao, Zhiwei Zhang and Yi Li
Sensors 2025, 25(17), 5592; https://doi.org/10.3390/s25175592 - 8 Sep 2025
Viewed by 875
Abstract
This study presents a flame detection model that is based on real experimental data that were collected during turbopump hot-fire tests of a liquid rocket engine. In these tests, a MEMRECAM ACS-1 M40 high-speed camera—serving as an optical sensor within the test instrumentation [...] Read more.
This study presents a flame detection model that is based on real experimental data that were collected during turbopump hot-fire tests of a liquid rocket engine. In these tests, a MEMRECAM ACS-1 M40 high-speed camera—serving as an optical sensor within the test instrumentation system—captured plume images for analysis. To detect abnormal flame phenomena in the plume, a Gaussian support vector machine (SVM) model was developed using image features that were derived from both color and gradient information. Six representative frames containing visible flames were selected from a single test failure video. These images were segmented in the YCbCr color space using the k-means clustering algorithm to distinguish flame and non-flame pixels. A 10-dimensional feature vector was constructed for each pixel and then reduced to five dimensions using the Maximum Relevance Minimum Redundancy (mRMR) method. The reduced vectors were used to train the Gaussian SVM model. The model achieved a 97.6% detection accuracy despite being trained on a limited dataset. It has been successfully applied in multiple subsequent engine tests, and it has proven effective in detecting ablation-related anomalies. By combining real-world sensor data acquisition with intelligent image-based analysis, this work enhances the monitoring capabilities in rocket engine development. Full article
(This article belongs to the Special Issue Sensors and Advanced Sensing Techniques for Computer Vision Applications: Second Edition)
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29 pages, 8563 KB  
Article
A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion
by Yadong Yao, Yurui Zhang, Zai Liu and Heming Yuan
Sensors 2025, 25(14), 4399; https://doi.org/10.3390/s25144399 - 14 Jul 2025
Viewed by 691
Abstract
In response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy [...] Read more.
In response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy C-means (FCM) clustering and multi-feature fusion. A three-dimensional feature space is constructed using B-channel pixels and fuzzy clustering with c = 3, justified by the distinct distribution patterns of these three regions in the image, enabling effective preliminary segmentation. To enhance accuracy, connected domain labeling combined with a circularity threshold is introduced to differentiate linear cracks from granular noise. Furthermore, a 5 × 5 neighborhood search strategy, based on crack pixel amplitude, is designed to restore the continuity of fragmented cracks. Experimental results on the Concrete Crack and SDNET2018 datasets demonstrate that the proposed algorithm achieves an accuracy of 0.885 and a recall rate of 0.891, outperforming DeepLabv3+ by 4.2%. Notably, with a processing time of only 0.8 s per image, the algorithm balances high accuracy with real-time efficiency, effectively addressing challenges, such as missed fine cracks and misjudged broken cracks in noisy environments by integrating geometric features and pixel distribution characteristics. This study provides an efficient unsupervised solution for bridge damage detection. Full article
(This article belongs to the Special Issue Sensors and Advanced Sensing Techniques for Computer Vision Applications: Second Edition)
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34 pages, 32280 KB  
Article
Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation?
by Georgios Simantiris, Konstantinos Bacharidis and Costas Panagiotakis
Sensors 2025, 25(12), 3586; https://doi.org/10.3390/s25123586 - 6 Jun 2025
Viewed by 983
Abstract
We present a novel methodology for generating and filtering synthetic Unmanned Aerial Vehicle (UAV) flood imagery to enhance the generalization capabilities of segmentation models. Our framework combines text-to-image synthesis and image inpainting, using curated prompts and real-world segmentation masks to produce diverse and [...] Read more.
We present a novel methodology for generating and filtering synthetic Unmanned Aerial Vehicle (UAV) flood imagery to enhance the generalization capabilities of segmentation models. Our framework combines text-to-image synthesis and image inpainting, using curated prompts and real-world segmentation masks to produce diverse and realistic flood scenes. To overcome the lack of human annotations, we employ an unsupervised pseudo-labeling method that generates segmentation masks based on floodwater appearance characteristics. We further introduce a filtering stage based on outlier detection in feature space to improve the realism of the synthetic dataset. Experimental results on five state-of-the-art flood segmentation models show that synthetic data can closely match real data in training performance, and combining both sources improves model robustness by 1–7%. Finally, we investigate the impact of prompt design on the visual fidelity of generated images and provide qualitative and quantitative evidence of distributional similarity between real and synthetic data. Full article
(This article belongs to the Special Issue Sensors and Advanced Sensing Techniques for Computer Vision Applications: Second Edition)
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