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Object Detection With YOLOv5 for Electric Utility Asset Inspection Using UAVs.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Object Detection With YOLOv5 for Electric Utility Asset Inspection Using UAVs./
作者:	Nguyen, Richard.
面頁冊數:	1 online resource (50 pages)
附註:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
標題:	Information technology. -
電子資源:	click for full text (PQDT)
ISBN:	9798381171495

Object Detection With YOLOv5 for Electric Utility Asset Inspection Using UAVs.
Nguyen, Richard.

Object Detection With YOLOv5 for Electric Utility Asset Inspection Using UAVs. - 1 online resource (50 pages)

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.S.)--California State University, Long Beach, 2023.

Includes bibliographical references

The inspection of electric utility assets is an important procedure for repair and hazard prevention such as wildfires. Traditionally, human workers inspect powerlines manually which is time consuming and potentially dangerous due to high elevation and high voltage. Manual inspection requires power to be shut off during the procedure which results in inconvenient blackouts for residents and businesses which can be exacerbated by time spent to diagnose faults and repair equipment. With recent developments in computer vision and artificial intelligence (AI) through the use of machine learning, the process of inspecting electric utility assets can be both expedited and made safer through the use of unmanned aerial vehicles (UAVs). This research is part of a larger project funded by Aerospace Corporation to employ a network of UAVs equipped with AI capable of automatic flight pathing and electric utility asset detection to remotely detect faulty equipment and locate it accurately for speedy repair. This thesis covers a comprehensive implementation of electric utility asset object detection models using the YOLOv5 algorithm. The models are trained on a large dataset of approximately 30,000 high resolution images capturing five class objects: crossarms, cutouts, insulators, poles, and transformers. Eight different model configurations of the YOLOv5 algorithm are trained using the dataset and scored against each other to assess performance and computational cost for deployment on UAVs.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381171495Subjects--Topical Terms:

559429
Information technology.
Subjects--Index Terms:

Computer visionIndex Terms--Genre/Form:

554714
Electronic books.

Object Detection With YOLOv5 for Electric Utility Asset Inspection Using UAVs.
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520 $a The inspection of electric utility assets is an important procedure for repair and hazard prevention such as wildfires. Traditionally, human workers inspect powerlines manually which is time consuming and potentially dangerous due to high elevation and high voltage. Manual inspection requires power to be shut off during the procedure which results in inconvenient blackouts for residents and businesses which can be exacerbated by time spent to diagnose faults and repair equipment. With recent developments in computer vision and artificial intelligence (AI) through the use of machine learning, the process of inspecting electric utility assets can be both expedited and made safer through the use of unmanned aerial vehicles (UAVs). This research is part of a larger project funded by Aerospace Corporation to employ a network of UAVs equipped with AI capable of automatic flight pathing and electric utility asset detection to remotely detect faulty equipment and locate it accurately for speedy repair. This thesis covers a comprehensive implementation of electric utility asset object detection models using the YOLOv5 algorithm. The models are trained on a large dataset of approximately 30,000 high resolution images capturing five class objects: crossarms, cutouts, insulators, poles, and transformers. Eight different model configurations of the YOLOv5 algorithm are trained using the dataset and scored against each other to assess performance and computational cost for deployment on UAVs.
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