Power System to Prevent External Damage Detection Method

Authors

  • Jiacheng Tang
  • Binglin Li

DOI:

https://doi.org/10.54097/0r9rwx85

Keywords:

Transmission line; YOLOv5s; multi-scale target; C3 feature extraction module; loss function.

Abstract

An improved YOLOv5s multi-scale target detection algorithm is proposed to address the increasingly threatening problem of foreign object intrusion in power transmission lines, which poses a risk to the safety and stable operation of the power grid. To enhance the model's ability to extract features of different sizes, a new C3 feature extraction module was designed. It uses deformable convolutional networks to replace the original fixed convolutional layers, and then introduces the ECA attention mechanism to take into account global context information, reducing the decrease in model detection accuracy caused by feature loss. An improved form of NWD fusion Wise IoU is proposed as the network's predicted box regression loss function, improving the model's generalization ability and its ability to detect features of small targets, and achieving faster network convergence speed. Experimental results show that the improved algorithm has increased the accuracy P, recall rate R, and average precision mAP by 5.21%, 4.71%, and 6.24%, respectively, compared to the original YOLOv5s model in detecting foreign object intrusion in power transmission lines, while also enhancing the detection performance of small targets.

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References

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Published

12-03-2024

Issue

Section

Articles

How to Cite

Power System to Prevent External Damage Detection Method. (2024). Academic Journal of Science and Technology, 9(3), 189-193. https://doi.org/10.54097/0r9rwx85

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