3 research outputs found
UDTIRI: An Open-Source Road Pothole Detection Benchmark Suite
It is seen that there is enormous potential to leverage powerful deep
learning methods in the emerging field of urban digital twins. It is
particularly in the area of intelligent road inspection where there is
currently limited research and data available. To facilitate progress in this
field, we have developed a well-labeled road pothole dataset named Urban
Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this
dataset will enable the use of powerful deep learning methods in urban road
inspection, providing algorithms with a more comprehensive understanding of the
scene and maximizing their potential. Our dataset comprises 1000 images of
potholes, captured in various scenarios with different lighting and humidity
conditions. Our intention is to employ this dataset for object detection,
semantic segmentation, and instance segmentation tasks. Our team has devoted
significant effort to conducting a detailed statistical analysis, and
benchmarking a selection of representative algorithms from recent years. We
also provide a multi-task platform for researchers to fully exploit the
performance of various algorithms with the support of UDTIRI dataset.Comment: Database webpage: https://www.udtiri.com/, Kaggle webpage:
https://www.kaggle.com/datasets/jiahangli617/udtir