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: 31 October 2025 | Viewed by 675

Special Issue Editors


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

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

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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 (1 paper)

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Research

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 378
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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