Underground Parking Lot Car Finder Image Dataset

Currently, with the continuous growth of urbanization and the number of cars owned, underground parking lots, as an important part of urban infrastructure, face challenges in parking space management and quick car location. Existing manual inspections and simple navigation signs are inefficient and error-prone, unable to meet the increasing parking demand and the requirement to enhance user experience. The Underground Parking Lot Car Finder Image Dataset aims to solve technical issues such as the difficulty in vehicle positioning and long car-finding times through the application of image recognition and navigation technology, fulfilling the business needs of smart parking lots for intelligent and refined management. Data collection uses professional high-definition camera equipment to capture parking lot environment images under different lighting and weather conditions. Through multiple rounds of strict manual annotation, consistency checks, and expert review for quality control, high accuracy and consistency of data are ensured. The annotation team consists of experts in the fields of traffic engineering and computer vision, with a team size of over 50 people. During data preprocessing, techniques such as denoising, contrast adjustment, and image enhancement are applied to improve data quality. Data is stored in JPG format, organized for easy query and analysis by location and date.