{'fraction_non_alphanumeric': 0.02459791863765374, 'fraction_numerical': 0.007568590350047304, 'mean_word_length': 5.373493975903615, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
OffRoadScene: An Open Database for Unstructured Road Detection Algorithms

We address the problem of unstructured road detection. This paper tries to build a database named OffRoadScene, which addresses the need for experimental data to quantitatively evaluate the performance of different unstructured road detection algorithms. OffRoadScene is comprised of two level of databases. In the first level, each frame document consists of not only image information, but also information of GPS (Global Position System), IMU (Inertial Measurement Unit) and laser scanner. In the second level, original images and corresponding benchmarks are offered. 550 series unstructured road images and 120 various scenarios of images are included currently. In addition, 13 video segments are in video segments file. In support of expanding OffRoadScene, we present a custom-made labeling software for assisting users who wish to add their own images. Finally, we explain how to use this database by evaluating some state-of-the-art unstructured road detection algorithms.