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Title Yolov8 For Pedestrian Detection: A Comparative Study For Pedestrian Detection
ID_Doc 62128
Authors Sivaraman G.; Sophiya E.; Diviya M.
Year 2024
Published 2024 3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024
DOI http://dx.doi.org/10.1109/ICEEICT61591.2024.10718372
Abstract This research analyzes the usefulness of YOLOv8 for robust pedestrian recognition in demanding real world situations. A custom-trained YOLOv8 model is evaluated against SSD models, displaying better accuracy in detecting and localizing human classes within images characterized by occlusions and variable scales. We analyze the model's performance across confidence thresholds, demonstrating a positive balance between precision and recall. While noting computational considerations, our findings demonstrate YOLOv8's potential for advancing pedestrian detection systems, opening the way for enhanced safety and efficiency in applications such as autonomous driving, surveillance, and smart city efforts. © 2024 IEEE.
Author Keywords Deep Learning; Mean Average Precision; Pedestrian Detection; SSD (Single Shot MultiBox Detector); YOLOv8(You Look Only Once)


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