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Sustainability of Intelligent Detection and New Sensor Technology
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Sustainability of Intelligent Detection and New Sensor Technology

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1927

Editors

Dr. Minjie Wan

E-Mail Website
Guest Editor
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: computational imaging; intelligent object detection; optical design
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaofang Kong

E-Mail Website
Guest Editor
National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: computer vision; underwater object detection; dynamic parameter testing

Special Issue Information

Dear Colleagues,

In recent years, the rapid advancement of intelligent detection systems and sensor technologies has transformed numerous industries, including environmental monitoring, underwater exploration, intelligent agriculture, and industrial automation. These innovations enable real-time, high-precision data collection and analysis, greatly enhancing decision-making processes. As the demand for smarter, more efficient systems has grown, the integration of intelligent detection and sensor technologies has become a critical component in shaping the future of industries and society.

While the potential of these technologies is immense, their sustainability remains a core concern. The development of energy-efficient, low-cost, and environmentally friendly sensors—along with intelligent systems capable of real-time operation with minimal human intervention—is crucial for their widespread adoption. Furthermore, addressing the challenges of data privacy, security, and long-term reliability is essential to fostering trust and supporting the continued advancement of these technologies.

This Special Issue aims to showcase state-of-the-art research on the sustainability of intelligent detection systems and emerging sensor technologies. We seek to explore innovations in sensor design, data processing techniques, and energy-efficient solutions, as well as examine how these technologies can contribute to a more sustainable future. Additionally, we encourage submissions that address the societal, ethical, and environmental implications of deploying such technologies across various applications.

Through this Special Issue, we hope to facilitate cross-disciplinary collaborations that drive the development of intelligent detection systems and sensor technologies—solutions that are not only innovative but also sustainable, resilient, and responsible.

Topics of interest include, but are not limited to, the following:

  • Advances in optical imaging for intelligent detection ;
  • Innovative computational approaches for intelligent detection ;
  • AI and machine learning in intelligent detection;
  • Intelligent detection in autonomous vehicles;
  • Robotics and intelligent detection for navigation;
  • Sustainable and smart sensors in intelligent detection;
  • Sensor fusion and multimodal perception in intelligent detection;
  • Energy-efficient sensor networks and edge computing;
  • Emerging technologies for intelligent detection sensors;
  • Sustainability challenges in advanced sensor fabrication;
  • Recyclability, circularity, and end-of-life management of sensor components;
  • AI-powered sensor optimization and self-calibration;
  • Applications of intelligent detection systems in agriculture, biomedicine, defense, geology, and environmental science.

Dr. Minjie Wan
Dr. Xiaofang Kong
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. Sustainability 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 2400 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

  • intelligent detection
  • sensor technologies
  • sensor fabrication
  • sensor fusion
  • sustainability
  • energy-efficient sensors
  • AI in intelligent detection
  • AI-powered sensor optimization
  • sensor design innovations
  • sensor data processing and analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

25 pages, 7164 KB  
Article
Underwater Image Enhancement and Small Object Detection Method Based on RBE-CycleGAN and MSFDC-Net
by Zongren Li, Chundong Xu, Wenjun Hui, Rui Chen and Xiaofang Kong
Sustainability 2026, 18(13), 6659; https://doi.org/10.3390/su18136659 - 1 Jul 2026
Viewed by 332
Abstract
Underwater object detection plays a vital role in marine exploration and resource exploitation. However, complex underwater environment leads to severe color deviation, blurring, and information loss of small targets, which greatly restrict detection performance. To address these problems, this paper integrates the Channel [...] Read more.
Underwater object detection plays a vital role in marine exploration and resource exploitation. However, complex underwater environment leads to severe color deviation, blurring, and information loss of small targets, which greatly restrict detection performance. To address these problems, this paper integrates the Channel Attention and Spatial Attention Block (CASAB) attention mechanism into residual blocks based on generative adversarial networks to correct color distortion and improve the clarity of degraded underwater images. For underwater small object detection, MobileNetV2 is selected as the backbone network within the Faster R-CNN framework, and a multi-scale feature fusion strategy is adopted to reduce feature loss caused by repeated downsampling. In the detection head, coordinate attention and parallel dilated convolution are further integrated to suppress background noise and expand the receptive field of feature extraction. Experimental results on the Underwater Robot Professional Contest (URPC) dataset demonstrate that the proposed method yields gains of 10.06%, 9.43%, and 12.29% in three evaluation metrics: Underwater Image Quality Measure (UIQM), Underwater Colour Image Quality Evaluation (UCIQE) and Natural Image Quality Evaluator (NIQE), together with 7.81% in Mean Average Precision (mAP) and an 8.57% increase in Mean Recall (mRecall). These results demonstrate the effectiveness of all improvements. Full article
(This article belongs to the Special Issue Sustainability of Intelligent Detection and New Sensor Technology)
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22 pages, 16026 KB  
Article
Attention-Enhanced and Multi-Scale Network for Image Tamper Detection and Localization
by Yuqin Zhang and Kan Ren
Sustainability 2026, 18(12), 6348; https://doi.org/10.3390/su18126348 - 22 Jun 2026
Viewed by 379
Abstract
The rapid proliferation of image editing tools poses unprecedented challenges to information sustainability and social trust, as malicious digital forgeries can easily contaminate public discourse, news reporting, and legal forensics. Advanced image editing techniques make image tampering increasingly difficult for the naked eye [...] Read more.
The rapid proliferation of image editing tools poses unprecedented challenges to information sustainability and social trust, as malicious digital forgeries can easily contaminate public discourse, news reporting, and legal forensics. Advanced image editing techniques make image tampering increasingly difficult for the naked eye to recognize, which requires highly accurate methods for detecting and localizing image tampering. In this paper, an end-to-end network model named AEM-Net is proposed. AEM-Net combines RGB and SRM features to enhance the model’s sensitivity to image details and potentially tampered regions through multi-scale feature extraction and fusion. AEM-Net consists of the HRNet-based Multiscale Feature Extraction Module and the Context-Aggregated Pyramid Localization Module (CAPLM). The multi-scale feature extraction module utilizes the Attentional Perceptual Feature Fusion Module to adaptively focus on the anomalous regions. In contrast, the CAPLM utilizes the Expanded Convolutional Feedback Enhancement Module to effectively exploit contextual feature information for achieving pixel-level localization of tampered regions. Experimental results on public benchmark datasets demonstrate that AEM-Net achieves superior performance compared with existing state-of-the-art methods. In particular, AEM-Net achieves an AUC/F1 score of 95.36%/67.19% on CasiaV1, 93.25%/79.75% on Coverage, and 87.36%/66.24% on NIST16, while requiring only 0.09 s to process a single image, demonstrating both high localization accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Sustainability of Intelligent Detection and New Sensor Technology)
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15 pages, 6259 KB  
Article
TopoAD: Resource-Efficient OOD Detection via Multi-Scale Euler Characteristic Curves
by Liqiang Lin, Xueyu Ye, Zhiyu Lin, Yunyu Kang, Shuwu Chen and Xiaolong Liu
Sustainability 2026, 18(3), 1215; https://doi.org/10.3390/su18031215 - 25 Jan 2026
Viewed by 584
Abstract
Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection [...] Read more.
Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection framework that leverages Euler Characteristic Curves (ECCs) extracted from intermediate convolutional activation maps and fuses them with standardized energy scores. Specifically, we employ a computationally efficient superlevel-set filtration with a local estimator to capture topological invariants, avoiding the high cost of persistent homology. Furthermore, we introduce task-adaptive aggregation strategies to effectively integrate multi-scale topological features based on the complexity of distribution shifts. We evaluate our method on CIFAR-10 against four diverse OOD benchmarks spanning far-OOD (Textures), near-OOD (SVHN), and semantic shift scenarios. Our results demonstrate that TopoAD-Gated achieves superior performance on far-OOD data with 89.98% AUROC on Textures, while the ultra-lightweight TopoAD-Linear provides an efficient alternative for near-OOD detection. Comprehensive ablation studies reveal that cross-layer gating effectively captures multi-scale topological shifts, while threshold-wise attention provides limited benefit and can degrade far-OOD performance. Our analysis demonstrates that topological features are particularly effective for detecting OOD samples with distinct structural characteristics, highlighting TopoAD’s potential as a sustainable solution for resource-constrained applications in texture analysis, medical imaging, and remote sensing. Full article
(This article belongs to the Special Issue Sustainability of Intelligent Detection and New Sensor Technology)
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