Foggy License Plates Worldwide: A Comprehensive Dataset

The "Foggy License Plates Worldwide" dataset is a specialized collection designed to advance the recognition and detection of vehicles and license plates under foggy conditions. This dataset includes 4420 2D-RGB images, featuring a diverse array of vehicles from Bangladesh, Thailand, Saudi Arabia, and other regions with English license plates. These images are derived from a secondary dataset and have been augmented using monocular depth estimation to simulate varying degrees of fog, offering a realistic set of data for challenging visibility scenarios.

The dataset is not limited to one type of vehicle but includes buses, trucks, CNG vehicles, motorcycles, cars, and standalone license plates, providing a broad spectrum for analysis. While the Bangladeshi subset contains 2754 annotated images, the dataset also includes 388 images from Thailand, 433 from regions with English license plates, and 845 from Saudi Arabia, though the latter does not come with annotations.

This variety is crucial for developing robust algorithms that can operate across different regions and vehicle types, especially in foggy conditions. The use of the Monodepth2 Network to artificially introduce fog effects based on depth estimation ensures that the dataset can mimic real-world scenarios, enhancing the development of automated systems for license plate recognition, vehicle detection, and traffic monitoring under adverse weather conditions. By offering a global perspective with plates from multiple countries, this dataset serves as an invaluable resource for researchers and developers in the field of computer vision, aiming to enhance the accuracy and reliability of systems in foggy environments.