Pothole Mix
Description
This dataset for the semantic segmentation of potholes and cracks on the road surface was assembled from 5 other datasets already publicly available, plus a very small addition of segmented images on our part. To speed up the labeling operations, we started working with depth cameras to try to automate, to some extent, this extremely time-consuming phase. The main dataset is composed of 4340 (image,mask) pairs at different resolutions divided into training/validation/test sets with a proportion of 3340/496/504 images equal to 77/11/12 percent. This is the dataset used in the SHREC2022 competition and it is the dataset that allowed us to train the neural networks for semantic segmentation capable of obtaining the nice images and videos that you have probably already seen. Along the main dataset we also release a set of RGB-D videos consisting of 797 RGB clips and as many clips with their disparity maps, captured with the excellent OAK-D cameras we won for being finalists at the OpenCV AI Competition 2021. In an effort to achieve (semi-)automatic labeling for these clips, we postprocessed the disparity maps using classic CV algorithms and managed to obtain 359 binary mask clips. Obviously these masks are not perfect (they cannot be by definition, otherwise the problem of automatic road damage detection would not exist), but nonetheless we believe they are an excellent starting point to create, for example, new data augmentations (creating potholes on "intact road images" belonging to other standard road datasets) or to be used as textures in the creation of 3D scenes from which to extract large amounts of images/masks for the training of neural networks. You can have a preview of what you will find in these clips by watching this video showing the overlay of images and binary masks: http://deeplearning.ge.imati.cnr.it/genova-5G/video/pothole-mix-videos/pothole-mix-rgb-d-overlay-videos-concat.html Please take a look at the readme file inside the main dataset zipfile to have some more details about the single sub-datasets and their sources.