Road Surface Image and Video Dataset for Machine Learning Applications with Seasons

Abstract

Monitoring road surfaces is essential for ensuring the comfort and safety of all road users, including vehicles and pedestrians. Furthermore, the upkeep of the roadways will benefit from this knowledge. As a result of the unpredictable weather, the state of the roads worsens. Thus, producing an image dataset of the road surface for two seasons-summer and rainy-thus serves as the major goal of the suggested paper. Consequently, we produced photos and videos of various road surfaces, including paved and unpaved roads. These folders have two subfolders for potholes in the rainy and summer seasons. The dataset consists of 10 videos and 8484 pictures. For machine learning specialists working in the areas of automatic vehicle control and road surface monitoring, this dataset is quite helpful.