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AI for Emerging Image-Based Sensor Applications
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AI for Emerging Image-Based Sensor Applications

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

Deadline for manuscript submissions: 1 July 2026 | Viewed by 2616

Special Issue Editors

Prof. Dr. Sheng Huang

E-Mail Website
Guest Editor
School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China
Interests: computer vision; pattern recognition; medical image analysis; industrial intelligent inspection
Special Issues, Collections and Topics in MDPI journals
Dr. Shizheng Zhang

E-Mail Website
Guest Editor
College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
Interests: artificial intelligence; pattern recognition; image processing
Dr. Yi Zhang

E-Mail Website
Guest Editor
College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
Interests: computer vision; machine learning; pattern recognition

Special Issue Information

Dear Colleagues, 

Recent advancements in imaging technologies and sensor systems have led to an explosion of visual data across diverse domains, including autonomous systems, healthcare diagnostics, environmental monitoring, industrial automation, and smart cities. To unlock the full potential of this data, artificial intelligence (AI), particularly machine learning and deep learning techniques, has become indispensable for efficient processing, analysis, and interpretation.

This Special Issue aims to showcase cutting-edge research at the intersection of AI and image-based sensing. We invite original research articles and comprehensive reviews that explore novel AI methodologies, architectures, and applications specifically designed for emerging image sensor technologies and their real-world implementations.

Topics of interest include the following:

  • Deep learning models for image sensor data processing (e.g., CNNs, Transformers, and GANs).
  • AI-driven image enhancement, restoration, and super-resolution.
  • Real-time AI inference on edge devices with image sensors.
  • AI for hyperspectral, multispectral, thermal, or 3D imaging sensors.
  • Machine learning for event-based (neuromorphic) vision sensors.
  • AI-powered computer vision applications in robotics, surveillance, or medical imaging.
  • Efficient AI algorithms for low-power and resource-constrained sensor platforms.
  • Explainable AI (XAI) and robustness in image-based sensor systems.
  • Fusion of image sensor data with other modalities using AI.

Prof. Dr. Sheng Huang
Dr. Shizheng Zhang
Dr. Yi Zhang
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-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
  • image sensors
  • deep learning
  • computer vision

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Published Papers (4 papers)

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Research

19 pages, 32868 KB  
Article
Bias Calibration for Semi-Supervised Continual Learning
by Zhong Ji, Zhanyu Jiao, Deyu Miao and Chen Tang
Sensors 2026, 26(8), 2366; https://doi.org/10.3390/s26082366 - 11 Apr 2026
Viewed by 516
Abstract
In sensor-centric fields like healthcare, environmental monitoring, and industry, image classification is key to turning visual sensor data into actionable insights. Sensor-generated dynamic streaming data poses significant challenges for traditional static image classification models due to the continuous emergence of new categories, distribution [...] Read more.
In sensor-centric fields like healthcare, environmental monitoring, and industry, image classification is key to turning visual sensor data into actionable insights. Sensor-generated dynamic streaming data poses significant challenges for traditional static image classification models due to the continuous emergence of new categories, distribution shifts, and limited edge storage. With sensor streaming data facing label scarcity and high annotation costs, semi-supervised continual learning is essential, leveraging unlabeled data for incremental learning and reducing reliance on costly annotations. However, current semi-supervised continual learning methods rely on labeled data to generate pseudo-labels, leading to confirmation and relational biases. To mitigate these dual biases, we propose a Bias Calibration method based on nearest-neighbor semi-supervised continual learning, which integrates and adapts Confidence-Enhanced Learning (originally introduced for static datasets) and Guided Contrastive Learning. Specifically, the Confidence-Enhanced Learning aims to reduce competition among similar classes and penalizes low-confidence predictions, thereby generating high-confidence pseudo-labels for unlabeled data and mitigating confirmation bias. Guided Contrastive Learning constructs a pseudo-label graph and a feature representation graph, using the pseudo-label graph to optimize the feature representation graph, thereby improving class discrimination and reducing feature bias. Experiments on CIFAR-10, CIFAR-100, and ImageNet-100 show that our method significantly outperforms existing approaches, enhancing classification performance with partial labeling. Full article
(This article belongs to the Special Issue AI for Emerging Image-Based Sensor Applications)
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28 pages, 15951 KB  
Article
Local–Global Aware Concept Bottleneck Models for Interpretable Image Classification
by Ci Liu, Zijie Lin and Chen Tang
Sensors 2026, 26(6), 1833; https://doi.org/10.3390/s26061833 - 14 Mar 2026
Viewed by 635
Abstract
Concept Bottleneck Models facilitate interpretable image classification by predicting human-understandable concepts prior to class labels. However, when constructed upon CLIP, they exhibit unreliable concept scores stemming from CLIP’s global representation bias and insufficient region-level sensitivity, which severely constrain their effectiveness in sensor-driven applications [...] Read more.
Concept Bottleneck Models facilitate interpretable image classification by predicting human-understandable concepts prior to class labels. However, when constructed upon CLIP, they exhibit unreliable concept scores stemming from CLIP’s global representation bias and insufficient region-level sensitivity, which severely constrain their effectiveness in sensor-driven applications like remote sensing and medical imaging where localized visual evidence is critical. To mitigate this, we propose the Local–Global Aware Concept Bottleneck Model (LGA-CBM), which improves concept prediction through a training-free refinement pipeline. Building on initial CLIP-derived concept scores, LGA-CBM incorporates three key components: a Dual Masking Guided Concept Score Refinement (DMCSR) module that exploits attention weights to strengthen region–concept alignment; a Local-to-Global Concept Reidentification (L2GCR) strategy to harmonize local and global activations; and a Similar Concepts Correction Mechanism (SCCM) integrating Grounding DINO for fine-grained disambiguation. A sparse linear layer then maps the refined concepts to class labels, enabling highly interpretable classification with minimal concept usage. Experiments across six benchmark datasets demonstrate that LGA-CBM consistently achieves state-of-the-art performance in both accuracy and interpretability, producing explanations that align closely with human cognition. Full article
(This article belongs to the Special Issue AI for Emerging Image-Based Sensor Applications)
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18 pages, 2343 KB  
Article
VMESR: Variable Mamba-Enhanced Super-Resolution for Real-Time Road Scene Understanding with Automotive Vision Sensors
by Hongjun Zhu, Wanjun Wang, Chunyan Ma and Rongtao Hou
Sensors 2026, 26(5), 1683; https://doi.org/10.3390/s26051683 - 6 Mar 2026
Viewed by 534
Abstract
Automotive vision systems depend critically on front-view cameras, whose image quality frequently degrades under adverse conditions such as rain, fog, low illumination, and rapid motion. To address this challenge, we propose VMESR, a variable mamba-enhanced super-resolution network that integrates a selective state-space model [...] Read more.
Automotive vision systems depend critically on front-view cameras, whose image quality frequently degrades under adverse conditions such as rain, fog, low illumination, and rapid motion. To address this challenge, we propose VMESR, a variable mamba-enhanced super-resolution network that integrates a selective state-space model into a lightweight super-resolution architecture. By serializing 2D feature maps and applying variable-depth mamba blocks, VMESR captures long-range dependencies with linear complexity. A multi-scale feature extractor, enhanced residual modules equipped with a convolutional block attention module, and dense fusion connections work together to improve the recovery of high-frequency details. Extensive experiments demonstrate that VMESR achieves competitive performance in both objective metrics and perceptual quality compared to state-of-the-art methods, while significantly reducing parameter counts and computational cost. VMESR provides a practical balance between efficiency and reconstructive accuracy, offering a deployable super-resolution solution for embedded automotive sensors and enhancing the robustness of autonomous driving perception pipelines. Full article
(This article belongs to the Special Issue AI for Emerging Image-Based Sensor Applications)
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16 pages, 32370 KB  
Article
ATDIOU: Arctangent Differential Loss Function for Bounding Box Regression
by Qiang Tang, Hao Qiang, Yuan Tian, Xubin Feng, Wei Hao and Meilin Xie
Sensors 2026, 26(5), 1545; https://doi.org/10.3390/s26051545 - 1 Mar 2026
Viewed by 479
Abstract
Object detection is a fundamental task in computer vision. Bounding box regression (BBR) losses are critical to detector performance. However, evaluation measures that rely on the Intersection over Union (IoU) between the predicted and ground truth boxes are highly sensitive to positional deviations, [...] Read more.
Object detection is a fundamental task in computer vision. Bounding box regression (BBR) losses are critical to detector performance. However, evaluation measures that rely on the Intersection over Union (IoU) between the predicted and ground truth boxes are highly sensitive to positional deviations, which can hinder optimization. To alleviate this issue, we propose ATDIoU, a novel arctangent-differential loss for bounding-box regression. ATDIoU computes distance similarity between a predicted and a ground truth box by modeling the distances between their corresponding vertices as a two-dimensional arctangent differential distribution (ATD). This arctangent differential-based design mitigates bounding box drift and reduces sensitivity to localization errors. As a result, it guides the model to learn target positions more effectively. We evaluate ATDIoU by integrating it into YOLOv6 and conducting experiments on PASCAL VOC and VisDrone2019. The results demonstrate that ATDIoU yields improvements of 1.4% and 0.7% in mean average precision (mAP) relative to MPDIoU. Full article
(This article belongs to the Special Issue AI for Emerging Image-Based Sensor Applications)
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