DEVELOPMENT OF A COMPUTER VISION SYSTEM USING YOLOV8 FOR DETECTING AND COUNTING THE NUMBER OF PEOPLE ENTERING AND EXITING. (#870)
Read ArticleDate of Conference
July 17-19, 2024
Published In
"Sustainable Engineering for a Diverse, Equitable, and Inclusive Future at the Service of Education, Research, and Industry for a Society 5.0."
Location of Conference
Costa Rica
Authors
León León, Ryan Abraham
Olivares Garcia, Hans Anderson
Tiña Pérez, Zamyr Edú
Abstract
This study employed YOLOv8, an advanced neural network, to develop a real-time artificial vision system for detecting and counting people. A total of 7250 images were collected using Roboflow to train the model, enhancing its accuracy through data augmentation techniques. The training process leveraged a Tesla T4 GPU on Google Colab for accelerated processing. The system achieved an average accuracy of 94.6%, with peaks reaching 100% at specific times, albeit encountering some false positives. These findings underscore YOLOv8's effectiveness in enhancing security and crowd management, suggesting future enhancements in model confidence and image quality could further improve performance.