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Computer Vision and Pattern Recognition Based on Sensing Technology
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Computer Vision and Pattern Recognition Based on Sensing Technology

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 3746

Special Issue Editor

Dr. Xiao Yu

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300387, China
Interests: artificial intelligence; machine learning; data processing; pattern recognition; image processing; image analysis; Industrial development planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancement of computing hardware and sensor technologies such as imaging technologies, applications of artificial intelligence like computer vision (CV) and pattern recognition (PR) are being increasingly applied in fields such as object inspection and identification, information security monitoring, and analysis prediction. Data analysis based on sensors and images has enhanced the precision and efficiency of intelligent monitoring, detection, prediction, and identification. However, challenges remain in terms of adaptability to complex environments, data utilization, and model generalization capabilities. Researchers are exploring methods such as adaptive feature extraction and multimodal algorithm fusion to optimize model robustness and adaptability. With advancements in these technologies, the application of sensors and artificial intelligence in production, safety, and daily life will become more precise and efficient, providing strong support for intelligent detection, risk warning, and automated decision-making.

This Special Issue explores innovative solutions that use advanced artificial intelligence and computer vision technologies to address challenges in real-world applications. Topics of interest include, but are not limited to, the following:

Object detection and identification；

Image classification and analysis；

Computer vision；

Intelligent sensors and data analysis;

Image classification and detection technologies;

Fiber optic sensors;

Image sensors.

Dr. Xiao Yu
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.

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

  • artificial intelligence
  • computer vision
  • image analysis
  • intelligent sensors

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Published Papers (3 papers)

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Research

26 pages, 3544 KB  
Article
Quick Response Code Verification Using Anti-Counterfeiting Pattern and Multi-Feature Fusion Network
by Ke Sun, Zhongyuan Guo and Hong Zheng
Sensors 2026, 26(10), 3067; https://doi.org/10.3390/s26103067 - 12 May 2026
Viewed by 417
Abstract
Quick response codes are widely used as anti-counterfeiting labels in the field of product packaging, but they are easily illegally copied. Thus, this paper introduces a quick response code verification method that combines an anti-counterfeiting pattern with a deep feature fusion network. Firstly, [...] Read more.
Quick response codes are widely used as anti-counterfeiting labels in the field of product packaging, but they are easily illegally copied. Thus, this paper introduces a quick response code verification method that combines an anti-counterfeiting pattern with a deep feature fusion network. Firstly, a specialized anti-counterfeiting quick response code is designed, composed of a standard quick response code and an anti-counterfeiting pattern, which is essentially a fine-grained random texture distribution sensitive to copying. Next, the anti-counterfeiting patterns are overlapped and divided into blocks during the data processing, which effectively expands the data volume and avoids the interference of pattern content on the authenticity identification. Then, a convolutional self-learning preprocessing layer is employed to initially learn the feature information that represents the difference between authenticity and forgery. Finally, a multi-feature fusion convolutional neural network is proposed to identity the authenticity of anti-counterfeiting patterns. The proposed network comprises two branches, facilitating multi-scale feature extraction and fusion. The effectiveness of the proposed approach is evaluated on a self-constructed quick response code dataset, and the experimental results demonstrate that the proposed approach outperforms traditional knowledge engineering methods and similar deep learning methods. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Sensing Technology)
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19 pages, 2512 KB  
Article
Fusion of Transformer and RBF for Anomalous Traffic Detection in Sensor Networks
by Aibing Dai, Jianwei Guo, Yuanyuan Hou and Yiou Wang
Sensors 2026, 26(2), 515; https://doi.org/10.3390/s26020515 - 13 Jan 2026
Cited by 1 | Viewed by 594
Abstract
With the widespread adoption of the Internet of Things (IoT) and smart devices, the volume of data generated in sensor networks has increased dramatically, with diverse and structurally complex types that pose growing security risks. Anomaly detection in sensor networks has become a [...] Read more.
With the widespread adoption of the Internet of Things (IoT) and smart devices, the volume of data generated in sensor networks has increased dramatically, with diverse and structurally complex types that pose growing security risks. Anomaly detection in sensor networks has become a key technology for ensuring system stability and secure operation. This paper proposes a sensor anomaly detection model, termed RESTADM, which integrates a Transformer and a Radial Basis Function (RBF) neural network. The model first employs the Transformer to effectively capture the temporal dependencies in sensor data and then uses the RBF neural network to accurately identify anomalies. Experimental results on two public benchmark datasets, SMD and PSM, demonstrate the state-of-the-art performance of RESTADM. Our model achieves impressive F1-scores of 98.56% on SMD and 97.70% on PSM. This represents a statistically significant improvement compared to a range of baseline algorithms, including traditional models like CNN and LSTM, as well as the standard Transformer model. This validates the effectiveness of our proposed Transformer-RBF fusion, confirming the model’s high accuracy and robustness and offering an efficient security solution for intelligent sensing systems. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Sensing Technology)
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13 pages, 706 KB  
Article
Enhancing 3D Face Recognition: Achieving Significant Gains via 2D-Aided Generative Augmentation
by Cuican Yu, Zihui Zhang, Huibin Li and Chang Liu
Sensors 2025, 25(16), 5049; https://doi.org/10.3390/s25165049 - 14 Aug 2025
Cited by 1 | Viewed by 2198
Abstract
The development of deep learning-based 3D face recognition has been constrained by the limited availability of large-scale 3D facial datasets, which are costly and labor-intensive to acquire. To address this challenge, we propose a novel 2D-aided framework that reconstructs 3D face geometries from [...] Read more.
The development of deep learning-based 3D face recognition has been constrained by the limited availability of large-scale 3D facial datasets, which are costly and labor-intensive to acquire. To address this challenge, we propose a novel 2D-aided framework that reconstructs 3D face geometries from abundant 2D images, enabling scalable and cost-effective data augmentation for 3D face recognition. Our pipeline integrates 3D face reconstruction with normal component image encoding and fine-tunes a deep face recognition model to learn discriminative representations from synthetic 3D data. Experimental results on four public benchmarks, i.e., the BU-3DFE, FRGC v2, Bosphorus, and BU-4DFE databases, demonstrate competitive rank-1 accuracies of 99.2%, 98.4%, 99.3%, and 96.5%, respectively, despite the absence of real 3D training data. We further evaluate the impact of alternative reconstruction methods and empirically demonstrate that higher-fidelity 3D inputs improve recognition performance. While synthetic 3D face data may lack certain fine-grained geometric details, our results validate their effectiveness for practical recognition tasks under diverse expressions and demographic conditions. This work provides an efficient and scalable paradigm for 3D face recognition by leveraging widely available face images, offering new insights into data-efficient training strategies for biometric systems. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Sensing Technology)
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