Optimization of Vehicle Detection at Intersections Using the YOLOv5 Model

This study aims to analyze and evaluate the performance of the YOLOv5 model in detecting vehicles at intersections to optimize traffic flow. The methods used in this research include training the YOLOv5 model with traffic datasets collected from various intersections and optimizing hyperparameters to achieve the best detection accuracy. The study results show that the optimized YOLOv5 model can detect multiple types of vehicles with high accuracy. The model achieved a detection accuracy of 85.47% for trucks, 87.12% for pedestrians, 86.54% for buses, 77.20% for cars, 80.48% for motorcycles, and 78.80% for bicycles. Significant improvements in detection performance were achieved compared to the default model. This research concludes that the optimization of the YOLOv5 model is effective in improving vehicle detection accuracy at intersections. Implementing this optimized model can significantly contribute to traffic management, reduce congestion, and improve road safety. It is expected that the implementation of this technology can be more widely applied for more efficient traffic management in various major cities.