Vehicle detection in complex scenarios based on YOLOv5

With the acceleration of urbanization, traffic congestion, accident risk, and other issues have become increasingly prominent, so vehicle recognition and optimization technology has become particularly important. Based on the YOLOv5 deep learning model, this study proposes a vehicle recognition and optimization method to address key issues in urban traffic management and intelligent driving. This study first tested YOLOv5 and trained and optimized the YOLOv5 model to enhance the model's ability to detect vehicles in various complex scenarios, such as rainy days and the coexistence of people and vehicles, which have a large number of interference factors. Through experimental verification, our improved model improves the accuracy of detection in such scenarios in vehicle recognition tasks, and finds the deficiencies in the experiment and proposes corresponding solutions.