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Electronics
Special Issues
Target Tracking and Recognition Techniques and Their Applications
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Target Tracking and Recognition Techniques and Their Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 1391

Special Issue Editors

Dr. Zhenhua Guo

E-Mail Website
Guest Editor
Tianyijiaotong Technology Ltd., Suzhou 215100, China
Interests: pattern recognition; computer vision; biometrics; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Liu

E-Mail Website
Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Interests: pattern recognition; computer vision; biometrics; Artificial Intelligence

Special Issue Information

Dear Colleagues,

The field of target tracking and recognition techniques has witnessed remarkable growth and evolution in recent years, driven by the rapid advancements in computer science, artificial intelligence, and sensor technologies. These techniques play a pivotal role in a wide range of applications across various domains, from ensuring national security to enabling smart transportation and facilitating medical diagnosis. This Special Issue aims to provide a comprehensive platform for researchers and practitioners to share their latest findings, innovative ideas, and practical experiences in this vibrant and crucial area.
1. Target Tracking Techniques.

  • Advanced algorithms;
  • Challenging environments;
  • Sensor integration.

2. Target Recognition Techniques.

  • Feature-based recognition;
  • Deep learning models;
  • Cross-modal recognition.

3. Applications of Target Tracking and Recognition.

  • Security and surveillance;
  • Intelligent transportation;
  • Medical imaging;
  • Industrial automation.

I/We look forward to receiving your contributions.

Dr. Zhenhua Guo
Dr. Feng Liu
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. Electronics 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

  • target tracking
  • target recognition
  • deep learning
  • sensor integration
  • applications
  • feature extraction
  • security surveillance
  • intelligent transportation
  • medical imaging
  • industrial automation

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Published Papers (2 papers)

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Research

18 pages, 1835 KB  
Article
Towards Robust Medical Image Segmentation with Hybrid CNN–Linear Mamba
by Xiao Ma and Guangming Lu
Electronics 2025, 14(23), 4726; https://doi.org/10.3390/electronics14234726 - 30 Nov 2025
Viewed by 277
Abstract
Problem: Medical image segmentation faces critical challenges in balancing global context modeling and computational efficiency. While conventional neural networks struggle with long-range dependencies, Transformers incur quadratic complexity. Although Mamba-based architectures achieve linear complexity, they lack adaptive mechanisms for heterogeneous medical images and demonstrate [...] Read more.
Problem: Medical image segmentation faces critical challenges in balancing global context modeling and computational efficiency. While conventional neural networks struggle with long-range dependencies, Transformers incur quadratic complexity. Although Mamba-based architectures achieve linear complexity, they lack adaptive mechanisms for heterogeneous medical images and demonstrate insufficient local feature extraction capabilities. Method: We propose Linear Context-Aware Robust Mamba (LCAR–Mamba) to address these dual limitations through adaptive resource allocation and enhanced multi-scale extraction. LCAR–Mamba integrates two synergistic modules: the Context-Aware Linear Mamba Module (CALM) for adaptive global–local fusion, and the Multi-scale Partial Dilated Convolution Module (MSPD) for efficient multi-scale feature refinement. Core Innovations: CALM module implements content-driven resource allocation through four-stage processing: (1) analyzing spatial complexity via gradient and activation statistics, (2) computing allocation weights to dynamically balance global and local processing branches, (3) parallel dual-path processing with linear attention and convolution, and (4) adaptive fusion guided by complexity weights. MSPD module employs statistics-based channel selection and multi-scale partial dilated convolutions to capture features at multiple receptive scales while reducing computational cost. Key Results: On ISIC2017 and ISIC2018 datasets, mIoU improvements of 0.81%/1.44% confirm effectiveness across 2D benchmarks. On the Synapse dataset, LCAR–Mamba achieves 85.56% DSC, outperforming the former best Mamba baseline by 0.48% with 33% fewer parameters. Significance: LCAR–Mamba demonstrates that adaptive resource allocation and statistics-driven multi-scale extraction can address critical limitations in linear-complexity architectures, establishing a promising direction for efficient medical image segmentation. Full article
(This article belongs to the Special Issue Target Tracking and Recognition Techniques and Their Applications)
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21 pages, 9325 KB  
Article
Lightweight Model Improvement and Application for Rice Disease Classification
by Tonglai Liu, Mingguang Liu, Chengcheng Yang, Ancong Wu, Xiaodong Li and Wenzhao Wei
Electronics 2025, 14(16), 3331; https://doi.org/10.3390/electronics14163331 - 21 Aug 2025
Viewed by 702
Abstract
The timely and correct identification of rice diseases is essential to ensuring rice productivity. However, many methods have drawbacks such as slow recognition speed, low recognition accuracy and overly complex models that are unfavorable for portability. Therefore, this study proposes an improved model [...] Read more.
The timely and correct identification of rice diseases is essential to ensuring rice productivity. However, many methods have drawbacks such as slow recognition speed, low recognition accuracy and overly complex models that are unfavorable for portability. Therefore, this study proposes an improved model for accurately classifying rice diseases based on a two-level routing attention mechanism and dynamic convolution based on the above difficulties. The model employs Alterable Kernel Convolution with dynamic, irregularly shaped convolutional kernels and Bi-level Routing Attention that utilizes sparsity to reduce parameters and involves a GPU-friendly dense matrix multiplication, which can achieve high-precision rice disease recognition while ensuring lightweight and recognition speed. The model successfully classified 10 species, including nine diseased and healthy rice, with 97.31% accuracy and a 97.18% F1-score. Our proposed method outperforms MobileNetV3-large, EfficientNet-b0, Swin Transformer-tiny and ResNet-50 by 1.73%, 1.82%, 1.25% and 0.67%, respectively. Meanwhile, the model contains only 4.453×106 parameters and achieves an inference time of 6.13 s, which facilitates deployment on mobile devices.The proposed MobileViT_BiAK method effectively identifies rice diseases while providing a lightweight and high-performance classification solution. Full article
(This article belongs to the Special Issue Target Tracking and Recognition Techniques and Their Applications)
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