Author(s)

Yang Lu ¹, Xuanrui Hu ¹, Xinzhe Zou ¹, Lei Han ¹

Affiliation(s)

¹ School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China

Corresponding Author

Yang Lu

ABSTRACT

Electricity is the basis for everyday life, and defect detection of insulators of high-voltage transmission lines is the key to ensuring power transmission. In order to overcome the problems of low accuracy of traditional target detection algorithms for small targets, weak representation ability of feature maps and little key information extraction, an improved CBAM attention-based insulator defect detection method CBAM-YOLOv8 based on YOLOv8 was proposed. The core is to apply the combination of Channel Attention Module and Spatial Attention Module to process the channel attention and spatial attention modules respectively for the input feature layers. Experiments show that the AP in the CPLID dataset is improved by 4.7% and the FPS is reduced by 2.7% compared with the original YOLOv8, which proves that the proposed method can maintain a high detection speed while greatly improving the detection accuracy, providing a more effective and safer solution for the detection of high-voltage transmission lines, and greatly reducing the labor cost and operation risk.

KEYWORDS

YOLOv8 Model, Image Enhancement, CBAM Attention

CITE THIS PAPER

Yang Lu, Xuanrui Hu, Xinzhe Zou, Lei Han, Research on Insulator Defect Detection Model Based on Improved YOLOv8. Journal of Electrotechnology, Electrical Engineering and Management (2024) Vol. 7: 11-17. DOI: http://dx.doi.org/10.23977/jeeem.2024.070302.

REFERENCES

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