Multi-Weather Pothole Detection (MWPD)

The Multi-Weather-Based Pothole Detection Dataset is a comprehensive collection of images designed to aid in developing and evaluating deep learning models for detecting road surface anomalies, particularly potholes, under diverse environmental conditions. Images captured under normal weather, and rainy conditions include variations in lighting, such as daytime, twilight, and nighttime settings. We tried to add high-quality images to ensure the clarity of road surface details. It facilitates the detection of small and partially obscured potholes. In the dataset, potholes are precisely annotated with bounding boxes. This dataset is meticulously curated to reflect weather scenarios, ensuring robust and adaptable pothole detection systems. We performed a few augmentation techniques, namely scaling, shifting, shearing, cropping, rotation, color perturbation, contrast adjustment, noise addition, and brightness adjustment through Roboflow platform.