Foreign object detection for transmission lines based on Swin Transformer V2 and YOLOX

Tang, Chaoli; Dong, Huiyuan; Huang, Yourui; Fang, Mengxiang; Fu, Jiahao

doi:10.1007/s00371-023-03004-8

Vis Comput

2023

DOI: 10.1007/s00371-023-03004-8

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Foreign object detection for transmission lines based on Swin Transformer V2 and YOLOX

Chaoli Tang

Huiyuan Dong

Yourui Huang

et al.

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2023

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Cited by 2 publications

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Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

Wang,

Yuan,

Zhou

et al. 2023

Applied Sciences

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The safe operation of high-voltage transmission lines ensures the power grid’s security. Various foreign objects attached to the transmission lines, such as balloons, kites and nesting birds, can significantly affect the safe and stable operation of high-voltage transmission lines. With the advancement of computer vision technology, periodic automatic inspection of foreign objects is efficient and necessary. Existing detection methods have low accuracy because foreign objects attached to the transmission lines are complex, including occlusions, diverse object types, significant scale variations, and complex backgrounds. In response to the practical needs of the Yunnan Branch of China Southern Power Grid Co., Ltd., this paper proposes an improved YOLOv8m-based model for detecting foreign objects on transmission lines. Experiments are conducted on a dataset collected from Yunnan Power Grid. The proposed model enhances the original YOLOv8m by incorporating a Global Attention Module (GAM) into the backbone to focus on occluded foreign objects, replacing the SPPF module with the SPPCSPC module to augment the model’s multiscale feature extraction capability, and introducing the Focal-EIoU loss function to address the issue of high- and low-quality sample imbalances. These improvements accelerate model convergence and enhance detection accuracy. The experimental results demonstrate that our proposed model achieves a 2.7% increase in mAP_0.5, a 4% increase in mAP_0.5:0.95, and a 6% increase in recall.

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Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

Wang,

Yuan,

Zhou

et al. 2023

Applied Sciences

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A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines

Yan,

Li,

Wang

et al. 2023

Entropy

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With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.

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Foreign object detection for transmission lines based on Swin Transformer V2 and YOLOX

Cited by 2 publications

References 42 publications

Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines

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