United States Department of Transportation
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Rapid and Accurate Assessment of Road Damage by Integrating Data from Mobile Camera Systems (MCS) and Mobile LiDAR Systems (MLS) [supporting dataset]
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2024-04-30
By Chen, Qi
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Abstract:In this project, we devised an innovative approach to produce highly detailed orthomosaics of road surfaces, with a spatial resolution as fine as millimeters, utilizing panoramic photos obtained from a mobile camera system combined with Structure-from-Motion technology. Our method emphasizes the necessity of accurately masking out the ego-vehicle (the vehicle carrying the camera), the sky, and any moving objects (such as cars, bicycles, and pedestrians) present in the street scenes captured by the photos. We employed a combination of deep learning, image processing techniques, and manual editing to perform this masking process. It was observed that removing these objects from the images facilitates precise photo alignment and often leads to a substantial enhancement in the quality of the orthomosaics. We tested our methodology at three different sites across two different islands with contrasting traffic conditions and surrounding environments (campus, urban, and rural). We found that the resulting orthomosaics are readily applicable for GIS analysis and the assessment of road conditions and damages. Moving forward, the methodology could be refined further by automating the masking process, particularly through the integration of deep learning models. Additionally, we discovered that the timing of photo capture significantly influences the quality of the orthomosaic, with midday proving to be a preferable time window compared to early morning or late afternoon to minimize shadow effects in the orthomosaics.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. This dataset has been curated to CoreTrustSeal's curation level "C. Initial Curation." To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.8083359). NTL staff last accessed this dataset at its repository URL on 2024-05-01. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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