Deep Learning-Based Pedestrian Detection and Analysis with YOLOv5

DOI

10.2991/978-94-6463-540-9_85How to use a DOI?

Keywords

Pedestrian Detection; YOLOv5; Computer Vision

Abstract

Fueled by the swift advancements in artificial intelligence, computer vision technology has found extensive applications across various domains. This article will focus on how to use the You Only Look Once version 5 (YOLOv5) to enhance the accuracy and efficiency of pedestrian detection. It begins by providing an overview of the development background and current research status of pedestrian detection, which can as method take into scene study. Subsequently, detailed insights into the network architecture of YOLOv5 and its constituent structures are presented. Finally, the study employs YOLOv5 to train the model and comprehensively analyzes and discusses the training outcomes. The experimentation is conducted using the roboflow dataset. The findings demonstrate that YOLOv5 exhibits robust performance in pedestrian identification across diverse scenarios, showcasing its effectiveness even in complex environments and occlusion scenarios. This research contributes to advancing the capabilities of pedestrian detection, thereby enhancing security monitoring and intelligent transportation systems.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xuchen Cui
PY  - 2024
DA  - 2024/10/16
TI  - Deep Learning-Based Pedestrian Detection and Analysis with YOLOv5
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 852
EP  - 862
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_85
DO  - 10.2991/978-94-6463-540-9_85
ID  - Cui2024
ER  -