Real-Time Pedestrian Surveillance System Based on Deep Learning Object Detecting Algorithm
DOI:
https://doi.org/10.52783/jns.v14.2762Keywords:
Pedestrian, Yolo, Deep Learning, Jetson Nano, Object DetectingAbstract
Smart crosswalks represent a novel approach to traffic management, employing the integration of Internet of Things (IoT) sensors and real-time control of traffic signals to mitigate the risks associated with pedestrian accidents. The advent of advanced deep learning algorithms for object detection has enhanced the feasibility of developing effective real-time pedestrian detection systems. The YOLO (You Only Look Once) algorithm, renowned for its high degree of accuracy and rapid object recognition, has undergone substantial enhancements. Notably, the YOLOv8 model has demonstrated substantial advancements in performance compared to its predecessors through the integration of the C2f module, PAN module, and the adoption of an anchor-free approach. This study has established a comprehensive pedestrian dataset for the evaluation of the efficacy of various YOLO versions, and has identified the optimal algorithm version for pedestrian detection through rigorous practical training. An on-device pedestrian detection system has been developed by deploying the trained algorithm onto a Jetson Nano board, and subsequent simulation tests have been conducted to verify the accurate detection of pedestrians.
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Copyright© by the authors. Licensee TAETI, Taiwan. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC) license (http://creativecommons.org/licenses/by/4.0/).
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