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Advanced Pattern Recognition: Intelligent Sensing and Imaging
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Advanced Pattern Recognition: Intelligent Sensing and Imaging

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

Deadline for manuscript submissions: 20 November 2026 | Viewed by 4194

Special Issue Editors

Prof. Dr. Zhe-Ming Lu

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
Interests: image/video processing and analysis; deep learning; data mining; information security
Special Issues, Collections and Topics in MDPI journals
Dr. Yangming Zheng

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
Interests: machine self-learning and evolution; image and video analysis and processing; deep neural networks and machine behavior
Dr. Hao Luo

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
Interests: embedded system; deep learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junbao Li

E-Mail Website
Guest Editor
School of Computing, Harbin Institute of Technology, Harbin 150001, China
Interests: machine learning; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yijia Zhang

E-Mail Website
Guest Editor Assistant
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
Interests: image/video processing and analysis; signal processing; information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Deep learning and a new round of artificial intelligence development have greatly promoted the development of pattern recognition in computer vision and intelligent sensing, e.g., human action pattern recognition based on acceleration sensors has become an emerging research direction in the field of pattern recognition.

This Special Issue is oriented towards intelligent algorithms and technologies in pattern recognition and sensing fields. The aim is to share the latest theoretical and technological achievements in intelligent sensing and pattern recognition, and to encourage scientists to publish their experimental and theoretical results in these fields, mainly those based on deep learning. The related application areas include the following: advanced pattern recognition; image and video analysis and processing; intelligent sensors; intelligent video surveillance; intelligent visual inspection; and security and privacy problems in sensing.

This Special Issue warmly welcomes the submission of studies related to the following research topics: vision research under new imaging conditions; biologically inspired computer vision research; multi-sensor fusion 3D vision research; visual scene understanding under high dynamic complex scenes; small-sample target recognition and understanding; and complex behavior semantic understanding. Electronic files and software providing full details of calculation and experimental procedures can be deposited as Supplementary Material. 

We look forward to receiving your submissions. 

Prof. Dr. Zhe-Ming Lu
Dr. Yangming Zheng
Dr. Hao Luo
Prof. Dr. Junbao Li
Guest Editors

Prof. Dr. Yijia Zhang
Guest Editor Assistant

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

  • computer vision
  • pattern recognition
  • intelligent sensing
  • deep learning
  • image and video analysis and processing
  • intelligent sensors
  • intelligent video surveillance
  • intelligent visual inspection
  • security and privacy in sensing

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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

18 pages, 11374 KB  
Article
CSGL-Former: Cross-Stripes Global–Local Fusion Transformer for Remote Sensing Image Dehazing
by Shuyi Feng, Xiran Zhang, Jie Yuan and Youwen Zhu
Sensors 2026, 26(7), 2102; https://doi.org/10.3390/s26072102 - 28 Mar 2026
Viewed by 387
Abstract
Remote sensing (RS) images are often degraded by atmospheric haze, which compromises both visual interpretation and downstream applications. To address this, we introduce CSGL-Former, a novel Cross-Stripes Global–Local Fusion Transformer for RS image dehazing. Our model efficiently captures anisotropic long-range dependencies using cross-stripes [...] Read more.
Remote sensing (RS) images are often degraded by atmospheric haze, which compromises both visual interpretation and downstream applications. To address this, we introduce CSGL-Former, a novel Cross-Stripes Global–Local Fusion Transformer for RS image dehazing. Our model efficiently captures anisotropic long-range dependencies using cross-stripes attention (CSA) and aggregates hierarchical global semantics via a Multi-Layer Global Aggregation (MLGA) module. In the decoder, global context is adaptively blended with fine-grained local features to restore intricate textures. Finally, inspired by the atmospheric scattering model, a soft reconstruction head restores the clear image by predicting spatially varying affine parameters, strictly preserving content fidelity while effectively removing haze. Trained end-to-end, CSGL-Former demonstrates a compelling balance of accuracy and efficiency. Extensive experiments on the RRSHID and SateHaze1K benchmarks show that our model achieves state-of-the-art or highly competitive performance against representative baselines. Ablation studies further validate the effectiveness of each proposed component. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition: Intelligent Sensing and Imaging)
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21 pages, 8478 KB  
Article
ClearSight-RS: A YOLOv5-Based Network with Dynamic Enhancement for Remote Sensing Small Target Detection
by Jie Yuan, Shuyi Feng and Hao Han
Sensors 2026, 26(1), 117; https://doi.org/10.3390/s26010117 - 24 Dec 2025
Cited by 2 | Viewed by 731
Abstract
Small target detection in remote sensing images faces challenges due to complex backgrounds, weak features, and large scale differences. This paper proposes an improved YOLOv5-based network, termed ClearSight-RS, with the full name “Clear and Accurate Small-target Insight for Remote Sensing”. As the name [...] Read more.
Small target detection in remote sensing images faces challenges due to complex backgrounds, weak features, and large scale differences. This paper proposes an improved YOLOv5-based network, termed ClearSight-RS, with the full name “Clear and Accurate Small-target Insight for Remote Sensing”. As the name implies, the network is dedicated to achieving clear feature perception and accurate target localization for small targets in remote sensing images. The improvements focus on three aspects: integrating an improved Dynamic Snake Convolution (DSConv) module into the backbone network to strengthen the extraction of small target boundaries and geometric features, as well as the expression of weak textures; embedding a Bi-Level Routing Attention (BRA) module in the Neck part to enhance target focusing and suppress background interference; and optimizing the detection head by retaining only shallow high-resolution feature layers for prediction, reducing feature loss and redundant computations. Experimental results show that, based on the VEDAI dataset, ClearSight-RS achieves the highest mAP for all 8 vehicle categories; based on the NWPU VHR-10 dataset, its overall mAP reaches 93.8%, significantly outperforming algorithms such as Faster RCNN and YOLOv5l; based on the DOTA dataset, the capability of the proposed BRA module in suppressing background interference and capturing small target features is demonstrated. The network balances accuracy and efficiency, performing prominently in detecting vehicles and multi-category small targets in complex backgrounds, verifying its effectiveness. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition: Intelligent Sensing and Imaging)
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16 pages, 17338 KB  
Article
MSRS-DETR: End-to-End Object Detection for Multi-Scale Remote Sensing
by Jie Yuan, Shuyi Feng and Hao Han
Sensors 2025, 25(18), 5734; https://doi.org/10.3390/s25185734 - 14 Sep 2025
Cited by 1 | Viewed by 2548
Abstract
Remote sensing imagery (RSI) object detection is critical to many applications, yet mainstream detectors analyse only spatial features and, because of spectral bias, fail to learn high-frequency information adequately, resulting in performance bottlenecks under cluttered backgrounds, distractors, and multi-scale targets, especially small ones. [...] Read more.
Remote sensing imagery (RSI) object detection is critical to many applications, yet mainstream detectors analyse only spatial features and, because of spectral bias, fail to learn high-frequency information adequately, resulting in performance bottlenecks under cluttered backgrounds, distractors, and multi-scale targets, especially small ones. To break these limitations, we propose MSRS-DETR, an end-to-end framework that deeply fuses spatial and frequency cues. The approach introduces three key innovations: (1) C2fFATNET, a frequency-attention-enhanced lightweight residual backbone that provides richer dual-domain features with fewer parameters; (2) an Entanglement Transformer Block (ETB) in the encoder that refines deep semantics via cross-domain frequency–spatial interaction and suppresses background interference; and (3) S2-CCFF, a shallow-feature-extended bidirectional fusion path that markedly improves the retention and utilisation of fine details for small objects. Experiments on HRSC2016 and ShipRSImageNet demonstrate the effectiveness and generalisation of this spatial–frequency paradigm: relative to the baseline, MSRS-DETR reduces parameters by 29.1%, boosts inference speed by 12.4% and 8.4%, and raises mAP50-95 by 1.69% and 2.16%, respectively. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition: Intelligent Sensing and Imaging)
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