ArticleHeliyon2024
Defect identification of electricity transmission line insulators based on the improved lightweight network model with computer vision assistance.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This work aims to ensure the safe operation of electricity transmission lines and reduce costs and maintenance difficulties. It studies the application of computer vision (CV) in the defect identification of electricity transmission lines. In addition, this work proposes a method to improve the lightweight network model to provide an effective identification model to solve the problem of electricity transmission line defects. Firstly, GraphCut segmentation and Laplace algorithms are employed to expand and sharpen the electricity transmission line image. Secondly, in light of the Depth Separable Convolution algorithm, a defect detection model for the electricity transmission line insulator is proposed based on the You Only Look Once 4 (YOLOv4) network. Moreover, MobileNetV1 is utilized to improve this lightweight network model. Finally, this work uses ImageNet, a large public dataset, to validate the proposed model experimentally. The research results reveal that: (1) In the model testing results, all research indicators of the model are greater than 90 %, indicating an excellent detection accuracy of this model. (2) The improved YOLOv4 model can increase the detection speed to 53 frames/s at the cost of 2.4 % accuracy. (3) After image sharpening, the improved YOLOv4 model has promoted the insulator defects' detection ability to a certain extent. The above outcomes suggest that the improved YOLOv4 model can predict more efficiently and accurately and reduce unnecessary false positives. This illustrates that the proposed model is feasible and is expected to be applied to the defect identification of electricity transmission lines in practice. These findings fully demonstrate this work's vital value in enhancing the prediction efficiency and accuracy, thus offering a strong preference for the defect identification of electricity transmission lines in practical applications.
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