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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: 31 May 2026 | Viewed by 988

Special Issue Editors


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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

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
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

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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

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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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Published Papers (1 paper)

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Research

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
Viewed by 818
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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