ARFNet: adaptive receptive field network for detecting insulator self-explosion defects
文献类型:期刊论文
作者 | Zhang, Ke3,4; Qian, Shaowei2,4; Zhou, Jianan1; Xie, Chengjun2![]() |
刊名 | SIGNAL IMAGE AND VIDEO PROCESSING
![]() |
出版日期 | 2022-04-15 |
关键词 | Insulator self-explosion Object detection Adaptive receptive field network Attention mechanism Deep learning |
ISSN号 | 1863-1703 |
DOI | 10.1007/s11760-022-02186-3 |
通讯作者 | Qian, Shaowei(qsw@mail.ustc.edu.cn) ; Du, Jianming(djming@iim.ac.cn) |
英文摘要 | Insulators are one of the critical components of high-altitude transmission lines. Insulator defects can lead to the failure of the power transmission system and even more severe consequences. Therefore, accurately locating and identifying insulator defects are particularly important. To address the problem of insulator information loss after insulation self-explosion and the large gap in insulator size, in this paper, we propose an effective and innovative module called adaptive receptive field network (ARFNet) to get proper context information for insulator self-explosion defects. ARFNet is an effective component that can be used in different networks to give the networks the ability to adapt the size of the receptive field through the attention mechanism. Besides, to further reduce the false detection rate, we also build a novel insulator dataset, including two categories of the whole insulator and the insulator self-explosion area. In addition, experiments show that our method can effectively improve detection accuracy and reduce the false detection rate. |
资助项目 | science and technology project of State Grid Corporation of China[5500-202140127A] |
WOS研究方向 | Engineering ; Imaging Science & Photographic Technology |
语种 | 英语 |
WOS记录号 | WOS:000782720500001 |
出版者 | SPRINGER LONDON LTD |
资助机构 | science and technology project of State Grid Corporation of China |
源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/128691] ![]() |
专题 | 中国科学院合肥物质科学研究院 |
通讯作者 | Qian, Shaowei; Du, Jianming |
作者单位 | 1.Hunan Univ, Changsha 410000, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.Anhui NARI Jiyuan Power Grid Technol Co Ltd, State Grid Power Res Inst, Hefei 230088, Peoples R China 4.Univ Sci & Technol China, Hefei 230026, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Ke,Qian, Shaowei,Zhou, Jianan,et al. ARFNet: adaptive receptive field network for detecting insulator self-explosion defects[J]. SIGNAL IMAGE AND VIDEO PROCESSING,2022. |
APA | Zhang, Ke,Qian, Shaowei,Zhou, Jianan,Xie, Chengjun,Du, Jianming,&Yin, Tao.(2022).ARFNet: adaptive receptive field network for detecting insulator self-explosion defects.SIGNAL IMAGE AND VIDEO PROCESSING. |
MLA | Zhang, Ke,et al."ARFNet: adaptive receptive field network for detecting insulator self-explosion defects".SIGNAL IMAGE AND VIDEO PROCESSING (2022). |
入库方式: OAI收割
来源:合肥物质科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。