دورية أكاديمية
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BIO-INSPIRED MULTIPLE SCALES PLACE RECOGNITION FOR ELECTRIC SUBSTATIONS

التفاصيل البيبلوغرافية
العنوان: BIO-INSPIRED MULTIPLE SCALES PLACE RECOGNITION FOR ELECTRIC SUBSTATIONS
المؤلفون: G. Wen, F. Zhou, H. Zhang, H. Pan, J. Cao, Z. Gao, Y. Liu, Z. Sun, L. Pei
المصدر: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-3-W1-2022, Pp 315-321 (2022)
بيانات النشر: Copernicus Publications, 2022.
سنة النشر: 2022
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Applied optics. Photonics
مصطلحات موضوعية: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
الوصف: We could get many helpful information and results from satellite remote sensing images and aerial images, including disaster monitoring, grid hidden danger identification, and electricity consumption management. In the recent years, novel computer vision and deep neural network have got a lot of attention in many fields because of mimicking mammalian cognitive mechanism as much as possible. With the in-depth of mammalian cognitive and motor mechanisms research, people trend to adopt these reliable and efficient methods for power grid management and maintenance.For utilizing computing resources and improving analysing efficiency flexibly, we propose an assessing and verification framework based on bio-inspired perception and understanding, which summarizes the most appropriate image scale in the electric facilities place recognition. The proposed framework consists of different scenes aerial images datasets, several electric facilities place recognition methods, and credible evaluating methods mimicking mammals. Firstly, we gather satellite remote images and aerial images of sufficient electric power facilities in the United States via Google Earth and other public datasets. Then, several typical place recognition methods are adopted to testing recognition ability of multi-scale perception results, like SAD, NetVLAD, and GIST descriptor. To get more reliable result, multi-units and multi-scenes experiments are implemented roundly. After all experiments and evaluations, we could get that the most appropriate image scale is 1000 m size and the highest recognition accuracy of electric power facilities location is 500 m. Conclusion in our article shows the recommended perception form and scale closest to human cognition in the power grid management and maintenance.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1682-1750
2194-9034
Relation: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-3-W1-2022/315/2022/isprs-archives-XLVI-3-W1-2022-315-2022.pdf; https://doaj.org/toc/1682-1750; https://doaj.org/toc/2194-9034
DOI: 10.5194/isprs-archives-XLVI-3-W1-2022-315-2022
URL الوصول: https://doaj.org/article/703d4149f03e49749fe8b802333fde27
رقم الأكسشن: edsdoj.703d4149f03e49749fe8b802333fde27
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:16821750
21949034
DOI:10.5194/isprs-archives-XLVI-3-W1-2022-315-2022