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The Method and Application of Discrimination and Search for Multi-point Grounding of N600 in Secondary Circuit of Voltage Transformer in Substation Read chapter Read first chapter

2024 | OriginalPaper | Chapter

An Improved Lightweight YOLOv5-Based Face Target Detection Algorithm

Authors : Tianlin Hui, Li Zhao

Published in: Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology

Publisher: Springer Nature Singapore

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Abstract

In this paper, an improved lightweight YOLOv5 based on a face target detection algorithm is proposed. First, build a small data set on top of the public data set. Next, the anchor frame is reset and the data annotation is standardized they improve the quality of the data set and improve the accuracy and speed of the model. The simulation results showed that the accuracy and mAP value of the improved YOLOv5 network reached 95.52% and 95.15%, respectively. They both have a 15% increase, and the computing speed has also increased by 19.00%. At last, this paper realizes faster and more accurate detection than the traditional YOLOv5 network.

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Metadata
Title
An Improved Lightweight YOLOv5-Based Face Target Detection Algorithm
Authors
Tianlin Hui
Li Zhao
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology

Print ISBN: 978-981-9727-56-8

Electronic ISBN: 978-981-9727-57-5

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-2757-5_44

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