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Deep Power Vision Technology and Intelligent Vision Sensors: 2nd Edition
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Deep Power Vision Technology and Intelligent Vision Sensors: 2nd Edition

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1140

Special Issue Editors

Prof. Dr. Ke Zhang

E-Mail Website
Guest Editor
Department of Electronic & Communication Engineering, School of Electrical and Electronic Engineering, North China Electric Power University, 619 Yonghuabei Dajie, Baoding 071000, China
Interests: computer vision; deep learning; power vision technology; facial attributes analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yincheng Qi

E-Mail Website
Guest Editor
Department of Electronic & Communication Engineering, School of Electrical and Electronic Engineering, North China Electric Power University, 619 Yonghuabei Dajie, Baoding 071000, China
Interests: power vision technology; power information technology; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An important component of power artificial intelligence technology, deep power vision technology is the application of deep learning-based computer vision technology in power systems. The electric power system is a key national infrastructure, and its safe and stable operation is linked to the national economy and people’s livelihoods, as well as the sustainable development of society. At present, an increasing number of inspection images and videos are obtained through vision sensors on helicopters, unmanned aerial vehicles, and robots. In order to improve the efficiency of power inspection and ensure the safe and stable operation of the electric power system, it has become a necessary to apply computer vision and deep learning to visually process the goals and defects of power plants, transmission lines, substations, and distribution lines in electric power systems.

The goal of this Special Issue is to provide a platform for the exchange of research, technical trends, and practical experience related to deep power vision technology and intelligent vision sensors. We are soliciting original papers of unpublished and completed research that is not currently under review by any other conference/magazine/journal. Topics of interest include but are not limited to the following:

  • Deep learning-based computer vision technology in transmission or distribution line inspection;
  • Deep learning-based computer vision technology in substation inspection;
  • Deep learning-based computer vision technology in power plant inspection;
  • Lightweight models for intelligent vision sensors;
  • Model compression for intelligent vision sensors.

Prof. Dr. Ke Zhang
Prof. Dr. Yincheng Qi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • power vision technology
  • deep learning
  • intelligent vision sensors

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Published Papers (1 paper)

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Research

16 pages, 4676 KiB  
Article
Lightweight Substation Equipment Defect Detection Algorithm for Small Targets
by Jianqiang Wang, Yiwei Sun, Ying Lin and Ke Zhang
Sensors 2024, 24(18), 5914; https://doi.org/10.3390/s24185914 - 12 Sep 2024
Cited by 1 | Viewed by 634
Abstract
Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets [...] Read more.
Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model’s feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model’s ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection. Full article
(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors: 2nd Edition)
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