Application of Computer Vision Technology in Defect Detection of Electrical Engineering Equipment and Strategies for Improving Recognition Accuracy
Main Article Content
Abstract
Addressing the limitations of traditional electrical equipment defect detection methods, including low efficiency, high missed detection rates, strong subjectivity, and insufficient real-time performance, this paper investigates the application of computer vision technology and corresponding strategies for improving recognition accuracy. Reliable defect identification is essential for ensuring the operational safety and electromagnetic reliability of modern power equipment and intelligent electromagnetic infrastructures. First, based on image processing, deep learning, and pattern recognition theories, a computer vision framework for electrical equipment defect detection is established, covering image acquisition, preprocessing, feature extraction, and defect classification. Second, through field investigations and experimental analysis, the visual characteristics of typical defects in transformers, circuit breakers, and insulators are systematically analyzed, while key challenges including complex environmental interference, limited small-sample recognition capability, and inadequate feature extraction are identified. Finally, an experimental platform is constructed using three representative categories of electrical engineering equipment, and comparative experiments are conducted to evaluate multiple optimization strategies. The results demonstrate that the proposed approach effectively improves defect recognition performance and provides a practical solution for intelligent electromagnetic equipment inspection, condition monitoring, and reliability enhancement in advanced power systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
National Energy Administration, “Report on the Safe and Stable Operation of the Power System (2022),” Beijing: China Electric Power Press; 2023.
Q. Shaomei, “Innovation in Modern Visual Communication Design Based on Computer Vision Technology,” International Journal of Knowledge Management (IJKM), vol. 22, no. 1, pp. 1-17, 2026, doi: 10.4018/IJKM.403418.
D. Liu, H. Zhang, D. Wei, et al., “Evaluating the contribution of city-wide street trees on PM2.5 removal based on Street View Imagery and computer vision technology,” Computers, Environment and Urban Systems, pp. 126102413-102413, 2026, doi: 10.1016/J.COMPENVURBSYS.2026.102413.
C. Oliver and D, “S J,D,” E P, et al. Contextualizing AI Evaluation in Anesthesiology: Interpreting Large Language Models and Computer Vision Metrics Across Clinical Use Cases—An Expert Statement from the Society of Technology in Anesthesia.Anesthesia & Analgesia, 2026, doi: 10.1213/ANE.0000000000007991.
H. Alsalman, P. Echelard, H. Batra, et al., “439 A Computer Vision Workflow for the Classification of Fibroepithelial Lesions of the Breast from Hematoxylin and Eosin-Stained Images.Laboratory Investigation,2026,106(3S):104724-104724,”, doi: 10.1016/J.LABINV.2025.104724.
Z. Anwar, S. Masood, and A. Srivastava, “Empowering Agriculture from Pixels to Diagnosis: A Review of Computer Vision Techniques for Plant Disease Detection.Archives of Computational Methods in Engineering, 2026,(prepublish):1-22,”, doi: 10.1007/S11831-026-10520-Y.
D. Liu, D. Wei, H. Ho C, et al., “Linking window-view nature exposure with health and wellbeing outcomes: Using photorealistic 3D city models and computer vision technique.Landscape and Urban Planning,2026,270105601-105601,”, doi: 10.1016/J.LANDURBPLAN.2026.105601.
Z. Ran, “Research on Traditional Chinese Painting Style Conversion Based on Computer Vision Technology.Journal of Cases on Information Technology (JCIT),2026,28(1):1-18,”, doi: 10.4018/JCIT.401355.
A. Sanchez, L. Herrera, A. Teixeira, et al., “Artificial intelligence-based waste detection in robotic surgery: a pilot validation study of computer vision technology.Journal of Robotic Surgery,2026,20(1):247-247,”, doi: 10.1007/S11701-026-03202-1.
L. Wang, M. Li, R. Zhao, et al., “Advanced Computer Vision Technology for Real-Time Rotation Angle Monitoring of Bridge Bearings in Structural Health Assessment.Buildings,2026,16(4):734-734,”, doi: 10.3390/BUILDINGS16040734.
Y. Kumar, K. Rahul, N. Arora, et al., “Computer Vision Technologies for Food Quality and Safety Assurance: Evaluating Trends and Analytical Results.Food Analytical Methods,2026,19(2):106-106,”, doi: 10.1007/S12161-026-03019-6.
A. Mohy A, H. Bassioni A, E. Elgendi O, et al., “Innovations in safety management for construction sites: the role of deep learning and computer vision techniques.Construction Innovation,2026,26(2):551-578,”, doi: 10.1108/CI-04-2023-0062.
S. Weiqing, “Efficient Use of Computer Vision Technology in Post-Production of Film and Television.Information Resources Management Journal (IRMJ),2026,38(1):1-17,”, doi: 10.4018/IRMJ.397925.
Y. Xiaoyi, N. Wenjing, G. Ye, et al., “Computer Vision Technology for Constructing a Swing Action Recognition and Feedback System.International Journal of Information Technologies and Systems Approach (IJITSA),2025,19(1):1-19,”, doi: 10.4018/IJITSA.396710.
S. Liu, J. Ge, L. Zhang, et al., “Assessment of tourist sun exposure using computer vision technology: A microscale analysis with 3D modeling and surveillance image data.Building and Environment,2026,289114091-114091,”, doi: 10.1016/J.BUILDENV.2025.114091.
H. Zhou, “Implementing computer vision technology and innovative practices in photography education.Systems and Soft Computing,2025,7200419-200419,”, doi: 10.1016/J.SASC.2025.200419.
M. Ruiz R and C. Rossi, “Automated detection of 20 Hz fin whale calls using computer vision techniques.Ecological Informatics,2025,92103499-103499,”, doi: 10.1016/J.ECOINF.2025.103499.
X. Li and A. Wang, “Retraction Note: Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques.Scientific Reports,2025,15(1):39180-39180,”, doi: 10.1038/S41598-025-27305-1.
G. Pereira, B. Silva, A. Miranda, et al., “56,” The use of computer vision and smart technologies to study feeding behaviour kinetics of nursery pigs in real time.Animal - Science Proceedings, vol. 16, no. 5, pp. 810-811, 2025, doi: 10.1016/J.ANSCIP.2025.09.057.
E. Adán and A. Adán, “Computer Vision for Glass Waste: Technologies and Sensors.Sensors,2025,25(21):6634-6634,”, doi: 10.3390/S25216634.
J. Park and S. Lee, “Do visual attributes of streetscapes affect car crashes? Applications of computer vision techniques and Machine learning.Travel Behaviour and Society,2026,42101153-101153,”, doi: 10.1016/J.TBS.2025.101153.
S. Babawale D, O. Adebimpe A, and V. Oladokun O, “Evaluation of computer vision techniques for quality inspection in casting manufacturing process.The International Journal of Advanced Manufacturing Technology,2025,141(3-4):1-16,”, doi: 10.1007/S00170-025-16665-7.
Kaur J S,Abhishek K,Rakesh S, et al.Applied Computer Vision through Artificial Intelligence[M].John Wiley & Sons, Inc.:2025-10-10:, doi: 10.1002/9781394272624.
A. Saudi A and A. Aljalal, “A hybrid SEM–MIP–and computer vision model for a non-destructive pore size evaluation in nanoporous alumina.The European Physical Journal Plus,2025,140(10):947-947,”, doi: 10.1140/EPJP/S13360-025-06886-W.
S. Yarmmohammadi, M. Rahman, V. Souza, et al., “27,” Automatic detection of nursery piglets behaviors and activities using computer vision technologies.Animal - Science Proceedings, vol. 16, no. 4, pp. 540-541, 2025, doi: 10.1016/J.ANSCIP.2025.08.182.
M. Rahman, V. Souza, T. Brandl B, et al., “56,” Automated monitoring of sow nursing behaviors and postures in farrowing crates through computer vision techniques.Animal - Science Proceedings, vol. 16, no. 4, pp. 595-596, 2025, doi: 10.1016/J.ANSCIP.2025.08.211.
A. Alaoui O, O. Farhaoui, M. Fethi R, et al., “Advancing emergency vehicle systems with deep learning: A comprehensive review of computer vision techniques.Intelligent Systems with Applications,2025,28200574-200574,”, doi: 10.1016/J.ISWA.2025.200574.
X. Cao, S. Lin, and W. Dai, “A Comparative Study and Analysis of Computer Vision Technology and 3D Scanning Technology in the Identification of Yao Ethnic Silver Ornaments.Journal of Big Data and Computing,2025,3(3):,”, doi: 10.62517/JBDC.202501313.
J. Kline, S. Afridi, E. Rolland A G, et al., “Studying collective animal behaviour with drones and computer vision.Methods in Ecology and Evolution,2025,16(10):2229-2259,”, doi: 10.1111/2041-210X.70128.
Mittal M,Gupta S.Biomedical Computer Vision:IoT and Sensor Technologies in Healthcare Informatics[M].De Gruyter:2025-08-19:, doi: 10.1515/9783111429458.