SHREC'22 track: Pothole and crack detection on road pavement using RGB-D images

The aim of this SHREC'20 track is to evaluate the performance of automatic algorithms for the recognition of potholes and cracks on road surfaces. Road safety is one of the top priorities of any public administration as well as being a subject of constant public scrutiny at the local and national levels as road degradation is one of the main causes of accidents. Currently, the scheduling of inspections and maintenance is entrusted to specialized personnel who require specific training and operate expensive and bulky machinery. This proposal aims to automate the detection of road deterioration by enabling timely monitoring of large areas of road pavement through the use of the latest Deep Learning techniques. The goal is to segment and recognize images and videos, using a training set generated with RGB-D images. The track is organized by IMATI CNR .

Dataset, ground truth and evaluation
The dataset is created using videos from RGB-D cameras and will be integrated using the existing (few) datasets available online. A training set of images and their segmentation masks and a test set of images (masks for the test set are held out for metrics assessment) will be sent to the participants. The proportion between the training set and the test set will be approximately 80-20. For each image in the test set, we ask the participants to segment the region where the pothole and the crack are present. The performance of the algorithms are evaluated using a classification rate (the simple accuracy score about where a pothole and/or crack is present in the image) and one or more semantic segmentation metrics (e.g. pixel accuracy, multiclass Dice, etc.) based on the area correctly recognized. Each participant is allowed to send us up to 3 outcomes for each task. More details soon!

Registration and instructions for competitors
Each participant is requested to register to the track by sending an email to Elia Moscoso Thompson (email: with the subject SHREC'22 track: Pothole and crack detection on road pavement using RGB-D images. Then, an answer will be sent to each participant with further instructions on how to download the models once the constest starts.

Further information

    More details soon!

Important dates [UPDATED - Jan 21th 2022]