IMPLEMENTATION OF THE YOLO MODEL FOR CROWD DENSITY ESTIMATION (CASE STUDY: RAILWAY STATION)
It is difficult to estimate the level of crowd density in busy and moving crowds at railway stations and conventional method and may result in inaccurate results. This final project aims to implement the You Only Look Once (YOLO) model to estimate crowd density and improve the performance of t...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/54352 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | It is difficult to estimate the level of crowd density in busy and moving crowds at railway
stations and conventional method and may result in inaccurate results. This final project aims
to implement the You Only Look Once (YOLO) model to estimate crowd density and improve
the performance of the YOLO model in estimating crowd density. Hyperparameter tuning
method is used by using the Tree-Structured Parzen Estimator (TPE) algorithm in the Optuna
framework to optimize the model. Hyperparameter tuning method produces the best
hyperparameter settings for the YOLO model on the IoU training threshold hyperparameter,
initial learning rate, final learning rate, SGD momentum and weight decay with values
respectively 0.7, 0.001, 0.6, 0.98 and 0.000123. Based on this final project, it is known that the
the YOLO model was successfully implemented in a crowd management system that focuses
on estimating the level of crowd density at railway stations without experiencing overfitting
and hyperparameter tuning provide better performance than the initial YOLO model. There is
an increase in model performance after optimization with an increase in mAP@0.5 and
mAP@0.5:0.95 metrics, respectively, 0.779 and 0.0675.
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