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

Monitoring highway surfaces is essential for ensuring the comfort and security of all road consumers, including jeeps and pedestrians. Furthermore, the maintenance of the roadways will benefit from this knowledge. As a result of the changeable weather, the state of the roads diminishes. Thus, producing an figure dataset of the road surface for two seasons-vacation and moist-thus serves as the bigger goal of the submitted paper. Consequently, we produced photos and videos of miscellaneous road surfaces, containing paved and unpaved roads. These folders have two subfolders for potholes in the moist and summer seasons. The dataset resides of 10 videos and 8484 pictures. For machine learning scholars working in the fields of automatic taxi control and road surface listening, this dataset is quite constructive.

Author(s) Details:

Sonali Bhutad,
Vishwakarma University, Pune, India.

Kailas Patil,
Vishwakarma University, Pune, India.

Please see the link here:

Keywords: Road surface observation, sustainable transportation, pothole identification, object detection, computer vision

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