Parking lot vehicle recognition system

In modern cities, as the demand for transportation increases, the number of vehicles continues to rise, thereby increasing the demand for multi-level parking garages. Unlike traditional open-air parking lots, multi-level parking garages need to consider the weight of different vehicles and require classification management of vehicles. Traditional weighing sensors typically rely on large-scale equipment and complex construction, resulting in higher costs.This paper takes a purely visual approach and utilizes ResNet18 to construct a vehicle classification method that divides vehicles into six categories. This method serves as an auxiliary system for multi-level parking garages, achieving purely visual processing of tasks that previously required multiple sensors. In the final training evaluation, our approach demonstrates 97.3% accuracy, 95.5% precision, 95% recall, and 96.3% F1 score. Additionally, we use TensorRT to accelerate the inference process, keeping the inference time within 2 milliseconds, enabling rapid inference that can be completed without parking.