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Image Processing and Pattern Recognition Based on Deep Learning for Sensing Applications—3rd Edition

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 760

Special Issue Editors


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Guest Editor
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computer Science, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania
Interests: image acquisition; image processing; feature extraction; image classification; image segmentation; artificial neural networks; deep learning; wireless sensor networks; unmanned aerial vehicles; data fusion; data processing in medicine; data processing in agriculture
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computer Science, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania
Interests: convolutional neural networks; artificial intelligence; medical image processing; biomedical optical imaging; computer vision; computerised monitoring; data acquisition; image colour analysis; texture analysis; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The pattern recognition used in analyzing and interpreting images in sensing applications is today closely tied to artificial intelligence and neural networks based on deep learning. The current trends in the use of neural networks include the following: (a) improvements within established families to enhance statistical performance and efficiency; (b) transfer learning; (c) the use of multiple networks in more complex systems; (d) the merging of decisions by individual networks; (e) the combination of efficient features with neural networks to improve detection and classification performance; and (f) the application of a multimodal approach based on data collection from various sensors. Additionally, combining neural networks with other artificial intelligence classifiers can also improve performance. New deep learning models have also been proven to improve detection, classification, and segmentation performances (for example, Visual Language Models (VLMs), Long Short-Term Memory (LSTM), Vision Transformer (ViT), and Large Language Models (LLMs)). Furthermore, sensors integrated with deep learning can improve sensorial applications in various fields, such as healthcare diagnostics, anomaly detection, traffic prediction, precision agriculture, and smart home systems, among others. Of particular importance in achieving high performance is accurate image collection by sensors. Special attention will be paid to data collection in various fields, including agriculture, medicine, environment, and restricted areas.

This Special Issue aims to publish original research contributions concerning new deep neural network-based approaches in image processing and pattern recognition for sensorial applications in various domains: remote sensing, crop monitoring, restricted zone monitoring, system support in medical diagnosis, emotion detection, and others.

The scope of the Special Issue includes (but is not limited to) the following research areas concerning image processing and pattern recognition, with the aid of new artificial intelligence techniques for sensorial applications:                                            

  • Image processing;
  • Sensors for various image generation: RGB, multispectral, thermal;
  • Collecting data and data fusion from different sensors;
  • Multimodal approaches;
  • Pattern recognition;
  • Image segmentation;
  • Object classification;
  • Neural networks;
  • Deep learning;
  • Decision fusion;
  • Systems based on multiple neural networks;
  • The detection of regions of interest from remote images;
  • Industry applications;
  • Sensorial domain applications;
  • Precision agriculture application;
  • Medical application;
  • The monitoring of protected areas;
  • Disaster monitoring and assessment.

Prof. Dr. Dan Popescu
Prof. Dr. Loretta Ichim
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

  • image processing
  • sensors for various image generation: RGB, multispectral, thermal
  • collecting data and data fusion from different sensors
  • multimodal approaches
  • pattern recognition
  • image segmentation
  • object classification
  • neural networks
  • deep learning
  • decision fusion
  • systems based on multiple neural networks
  • the detection of regions of interest from remote images
  • industry applications
  • sensorial domain applications
  • precision agriculture application
  • medical application
  • the monitoring of protected areas
  • disaster monitoring and assessment

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Published Papers (1 paper)

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Research

21 pages, 13872 KB  
Article
An Improved Lightweight Model for Protected Wildlife Detection in Camera Trap Images
by Zengjie Du, Dasheng Wu, Qingqing Wen, Fengya Xu, Zhongbin Liu, Cheng Li and Ruikang Luo
Sensors 2025, 25(23), 7331; https://doi.org/10.3390/s25237331 - 2 Dec 2025
Viewed by 516
Abstract
Effective monitoring of protected wildlife is crucial for biodiversity conservation. While camera traps provide valuable data for ecological observation, existing deep learning models often suffer from low accuracy in detecting rare species and high computational costs, hindering their deployment on edge devices. To [...] Read more.
Effective monitoring of protected wildlife is crucial for biodiversity conservation. While camera traps provide valuable data for ecological observation, existing deep learning models often suffer from low accuracy in detecting rare species and high computational costs, hindering their deployment on edge devices. To address these challenges, this study proposes YOLO11-APS, an improved lightweight model for protected wildlife detection. It enhances the YOLO11n by integrating the self-Attention and Convolution (ACmix) module, the Partial Convolution (PConv) module, and the SlimNeck paradigm. These improvements strengthen feature extraction under complex conditions while reducing computational costs. Experimental results demonstrate that YOLO11-APS achieves superior detection performance compared to the baseline model, attaining a precision of 92.7%, a recall of 87.0%, an mAP@0.5 of 92.6% and an mAP@0.5:0.95 of 62.2%. In terms of model lightweighting, YOLO11-APS reduces the number of parameters, floating-point operations, and model size by 10.1%, 11.1%, and 9.5%, respectively. YOLO11-APS achieves an optimal balance between accuracy and model complexity, outperforming existing mainstream lightweight detection models. Furthermore, tests on unseen wildlife data confirm its strong transferability and robustness. This work provides an efficient deep learning tool for automated wildlife monitoring in protected areas, facilitating the development of intelligent ecological sensing systems. Full article
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