DOI: 10.5683/sp3/5z8hii
title: Thermal camera data annotation for the Seeing Through the Fog dataset
title: Comparative Analysis of Sensor Performance in Autonomous Vehicles: Evaluating RGB, Thermal, and LiDAR Under Adverse Weather Conditions

description: This dataset contains manually annotated thermal frames from the Seeing Through the Fog dataset (https://light.princeton.edu/datasets/automated_driving_dataset/), with labeling performed only on the test set based on the sequence_based_split.json file, which contains the weather conditions and file names associated to each weather. The annotations follow the YOLO format, where each image has a corresponding .txt file containing the class ID and normalized bounding-box coordinates. The labeled classes include Car, Pedestrian, Large Vehicle, and Ridable Vehicle.

Some images have very poor visibility or no detectable objects at all. As a result, not all frames contain visible objects. For those cases, the label file is intentionally left empty or is not created. YOLO-based loaders naturally interpret missing or empty label files as images without annotations.

The thermal test set consists of 1,142 frames distributed across different weather conditions. Specifically, there are 128 frames each for Clear Day, Clear Night, Snow Day, Snow Night, Light Fog Day, Light Fog Night, Dense Fog Day, and Dense Fog Night. Rain conditions include 54 frames for Rain (Day) and 64 frames for Rain (Night). The variation in the number of frames across weather conditions reflects the original dataset’s availability and the differences in recorded sequences.

This labeled subset is designed for evaluating object detection performance on thermal imagery under diverse and challenging weather conditions, and for analyzing how visibility variations affect detection robustness.
description: The frame IDs based on the different weather conditions are listed bellow:

 "clear_night": {
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