Garage Vehicle Type Image Recognition Dataset

With the rapid development of urban traffic and the increasing demand for daily management of parking lots, quickly and accurately identifying vehicle types in garages has become a major challenge. Existing solutions usually rely on traditional sensors or RFID technology, but these technologies are susceptible to environmental influences and have limitations in identifying new and unregistered vehicles. This dataset aims to solve the technical problems of vehicle type recognition through high-quality vehicle image data, improving recognition accuracy and speed. The data is collected in different types of garage environments, including above-ground garages, underground garages, and multi-story garages. High-resolution cameras are used to ensure data consistency under different lighting conditions. Strict quality control is implemented, with multiple rounds of annotation and expert review to ensure high annotation accuracy. The annotation team is composed of professionals with rich experience in traffic management, with a team size of 20 people. Data preprocessing includes image denoising, brightness adjustment, and normalization, and is stored in a structured JPG format for easy access and use.