SHREC 2022: Pothole and crack detection in the road pavement using images and RGB-D data. (October 2022)

• Record Type: Journal Article
• Authors: Moscoso Thompson, Elia; Ranieri, Andrea; Biasotti, Silvia; Chicchon, Miguel; Sipiran, Ivan; Pham, Minh-Khoi; Nguyen-Ho, Thang-Long; Nguyen, Hai-Dang; Tran, Minh-Triet
• Is Part Of: Computers & graphics. Volume 107(2022)
• Page Start: 161
• Page End: 171
• Publication Date: 2022-10
• Subjects: Road monitoring -- Deep Learning -- Semantic segmentation -- RGB-D; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2022.07.018
• Languages: English
• ISSNs: 0097-8493

Abstract

Abstract: This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e., a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The methods are then evaluated on the 496 image/masks pairs in the validation set, on the 504 pairs in the test set and finally on 8 video clips. The analysis of the results is based on quantitative metrics for image segmentation and qualitative analysis of the video clips. The participation and the results show that the scenario is of great interest and that the use of RGB-D data is still challenging in this context. Graphical abstract: Highlights: A dataset of image/mask pairs and RGB-D video clips of road damage. A fair evaluation of the proposed methods implementing them on a single Jupyter notebook. A qualitative evaluation of videos of road damages from different points of view.