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:	Hui He, Xile Huang, Yuxuan Song, Zheng Zhang, Meng Wang, Bo Chen, Guangwei Yan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Insulator self-blast YOLOv4 Feature fusion Attention mechanism SENet Aerial images Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c3583fa8dd9048258df46cb8ca435d86
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id	oai:doaj.org-article:c3583fa8dd9048258df46cb8ca435d86
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spelling	oai:doaj.org-article:c3583fa8dd9048258df46cb8ca435d862021-12-04T04:35:03ZAn insulator self-blast detection method based on YOLOv4 with aerial images2352-484710.1016/j.egyr.2021.11.115https://doaj.org/article/c3583fa8dd9048258df46cb8ca435d862022-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721012622https://doaj.org/toc/2352-4847Due 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.Hui HeXile HuangYuxuan SongZheng ZhangMeng WangBo ChenGuangwei YanElsevierarticleInsulator self-blastYOLOv4Feature fusionAttention mechanismSENetAerial imagesElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 448-454 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Insulator self-blast YOLOv4 Feature fusion Attention mechanism SENet Aerial images Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Insulator self-blast YOLOv4 Feature fusion Attention mechanism SENet Aerial images Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hui He Xile Huang Yuxuan Song Zheng Zhang Meng Wang Bo Chen Guangwei Yan An insulator self-blast detection method based on YOLOv4 with aerial images
description	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.
format	article
author	Hui He Xile Huang Yuxuan Song Zheng Zhang Meng Wang Bo Chen Guangwei Yan
author_facet	Hui He Xile Huang Yuxuan Song Zheng Zhang Meng Wang Bo Chen Guangwei Yan
author_sort	Hui He
title	An insulator self-blast detection method based on YOLOv4 with aerial images
title_short	An insulator self-blast detection method based on YOLOv4 with aerial images
title_full	An insulator self-blast detection method based on YOLOv4 with aerial images
title_fullStr	An insulator self-blast detection method based on YOLOv4 with aerial images
title_full_unstemmed	An insulator self-blast detection method based on YOLOv4 with aerial images
title_sort	insulator self-blast detection method based on yolov4 with aerial images
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/c3583fa8dd9048258df46cb8ca435d86
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An insulator self-blast detection method based on YOLOv4 with aerial images

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