A study of nighttime vehicle detection algorithms

This dataset is from my master's thesis "A study of nighttime vehicle detection algorithms". It contains both raw data and preprocessed dataset ready to use. In the pictures below you can see how images were annotated.
Package contains 3 datasets for:

detection (YOLO, Pascal) - 2000 cars
classification of images - 100 000 images
classification of light intensity samples - 14 000 samples

Detection dataset has two classes: "front" and "rear".
Classification datasets have two classes: "There is a vehicle" or "There is no vehicle".
Dataset designed for vehicle detection at nighttime conditions, including images and recordings from the light intensity sensor. The task of vehicle detection at night is challenging due to limited visibility and reduced image quality. The Go Pro 5 camera and APDS9966 light intensity sensor, both of which are commercially available and popular for light recording applications, were used to create this dataset.
The choice of the Go Pro 5 camera and light intensity sensor as data sources was due to their popularity and availability in the market, which allows easy replication of the experiments and expand this dataset in the future. Two settings were used - urban and rural. The urban one is characterized by high light pollution. Examples of "front" class. Examples that are not classified as "front" class. Examples of "rear" class. Examples that are not classified as "rear" class.