•  
  •  
 

Journal of System Simulation

Abstract

Abstract: In response to the challenges present in the context of defect recognition in substations, such as complex substation defects and sample imbalance, an improved YOLOv5 algorithm was proposed. The Transformer model was introduced into the YOLOv5 network structure, leveraging the self-attention mechanism to capture long-range dependencies among features. A focal loss-based optimization was employed to improve the loss function, as well as the detection accuracy and robustness of defects of small sample substations. To meet the requirements of substation defect recognition, a dedicated dataset was constructed. A clustering algorithm was applied to the real annotation boxes to generate more accurate prior boxes. The genetic algorithm was utilized to select hyperparameters that are specifically tailored to the dataset, further enhancing the algorithm's performance. Experimental results demonstrate that the proposed algorithm achieves favorable performance in substation defect recognition tasks. In comparison to the traditional YOLOv5 algorithm, the proposed algorithm exhibits superior capabilities in recognizing complex substation defects and small sample targets.

First Page

2604

Last Page

2615

CLC

TP391

Recommended Citation

Xu Zhongkai, Liu Yanling, Sheng Xiaojuan, et al. Automatic Detection Algorithm for Typical Defects of Substation Based on Improved YOLOv5[J]. Journal of System Simulation, 2024, 36(11): 2604-2615.

DOI

10.16182/j.issn1004731x.joss.23-0914

Download

Share

COinS