Research on vehicle detection algorithm based on YOLOv5

With the surge of 5G technology, research on intelligent transportation systems has emerged as a forefront area, with vehicle detection technology at its core. This paper delves into vehicle detection utilizing YOLOv5, analyzing its performance through key metrics like training/validation losses, precision, recall, and mAP. The study highlights YOLOv5's superiority in vehicle detection, balancing swiftness with high accuracy and robustness. This work reinforces deep learning-based vehicle detection and presents valuable insights for future research endeavors.