Symmetry/Asymmetry Study in Object Detection

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 776

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Department of Data Analysis, Algebra University College, 10000 Zagreb, Croatia
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Special Issue Information

Dear Colleagues,

The field of object detection has experienced significant advancements in recent years, driven by the rapid development of machine learning and computer vision technologies. This Special Issue delves into the nuanced role that symmetry and asymmetry play in the effectiveness and accuracy of object detection algorithms. It explores theoretical foundations, methodological approaches, and practical applications, offering a comprehensive analysis of how symmetrical and asymmetrical features influence detection performance. By examining a range of case studies and experimental results, the authors provide valuable insights into optimizing detection systems for diverse real-world scenarios. This work serves as an essential resource for researchers and practitioners seeking to enhance their object detection capabilities through a deeper understanding of symmetry and asymmetry.

Prof. Dr. Leo Mrsic
Dr. Robert Kopal
Dr. Zlatan Morić
Dr. Nikola Protrka
Guest Editors

Manuscript Submission Information

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Keywords

  • object detection
  • symmetry
  • asymmetry
  • computer vision
  • machine learning
  • algorithm optimization
  • feature analysis
  • detection accuracy
  • pattern recognition
  • image processing
  • computational geometry
  • artificial intelligence
  • deep learning
  • detection performance
  • visual perception
  • case studies
  • experimental results
  • real-world applications
  • geometric features
  • algorithm evaluation

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Published Papers (1 paper)

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Research

19 pages, 13601 KiB  
Article
ETLSH-YOLO: An Edge–Real-Time Transmission Line Safety Hazard Detection Method
by Liangliang Zhao, Yu Zhang, Yinke Dou, Yangyang Jiao and Qiang Liu
Symmetry 2024, 16(10), 1378; https://doi.org/10.3390/sym16101378 - 16 Oct 2024
Viewed by 550
Abstract
Using deep learning methods to detect potential safety hazards in transmission lines is the mainstream method for power grid security monitoring. However, the existing model is too complex to adapt to edge device deployment and real-time detection. Therefore, an edge–real-time transmission line safety [...] Read more.
Using deep learning methods to detect potential safety hazards in transmission lines is the mainstream method for power grid security monitoring. However, the existing model is too complex to adapt to edge device deployment and real-time detection. Therefore, an edge–real-time transmission line safety hazard detection method (ETLSH-YOLO) was proposed to reduce the model’s complexity and improve the model’s robustness. Firstly, a re-parameterized Ghost efficient layer aggregation network (RepGhostCSPELAN) was designed to effectively fuse the feature information of different layers while enhancing the model’s expression ability and reducing the number of model parameters and floating-point operations. Then, a spatial channel decoupled downsampling block (CSDovn) was designed to reduce computational redundancy and improve the computational efficiency of the model. Then, coordinate attention (CA) was added in the process of multi-scale feature fusion to suppress the interference of complex background and improve the global perception ability of the model object. Finally, the Mish activation function was used to improve the network’s training speed, convergence, and generalization ability. The experimental results show that the mAP50 of this model improved by 1.73% compared with the baseline model, and the number of parameters and floating-point operations were reduced by 33.96% and 22.22%, respectively. This model lays the foundation for solving the dilemma of edge device deployment. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry Study in Object Detection)
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