Pavdistress: A Dataset of Selected Images for the Detection and Recognition of Asphalt Pavement Distress Using Deep Learning
Domaine:
socioeconomicgeospatialType de record:
dataset
Créateur:
Aou
Éditeur:
El
Éditeur:
Hôte:
The images in the Pavdistress dataset were captured by a road inspection vehicle in the Eastern region of Morocco. This dataset comprises 8,696 images of pavement deterioration.
Created from data collected by inspection vehicles, the Pavdistress dataset serves as a benchmark for detecting road damage. It is particularly useful for local authorities and road managers for cost-effectively monitoring road conditions.
Five types of road damage are listed in the dataset: edge cracks, longitudinal cracks, potholes, transverse cracks, and road markings.
Researchers can use this dataset as a benchmark to evaluate the performance of different algorithms in solving similar problems, such as image classification and object detection.
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