An insulator self-blast detection method based on YOLOv4 with aerial images
Due to long-term exposure to the natural environment, insulators are prone to self-blast, threatening the safety and reliability of transmission lines. Because of the different sizes of the insulator self-blast area and the complicated background, it is inevitable that the missed and false detection...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2022
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/c3583fa8dd9048258df46cb8ca435d86 |
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| Sumario: | Due to long-term exposure to the natural environment, insulators are prone to self-blast, threatening the safety and reliability of transmission lines. Because of the different sizes of the insulator self-blast area and the complicated background, it is inevitable that the missed and false detections occur. To solve this problem, this paper proposes a deep neural network called Mina-Net (Multi-Layer INformation Fusion and Attention Mechanism Network). Mina-Net is based on YOLOv4. First, the shallow feature map with more detailed texture information is fused into the feature pyramid. Then, an improved SENet is applied to recalibrate the features of different levels in the channel direction. The experimental results on the actual dataset show that Mina-Net has increased the average precision by 4.78% compared with the YOLOv4. |
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