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Title SHREC 2022: Pothole and Crack Detection in the Road Pavement using Images and RGB-D Data
Authors Elia Moscoso Thompson, Andrea Ranieri, Silvia Biasotti, Miguel Chicchón, Iván Sipirán, Minh-Khoi Pham, Thang-Long Nguyen-Ho, Hai-Dang Nguyen, Minh-Triet Tran
Publication date October 2022
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.
Pages 161-171
Volume 107
Journal name Computers & Graphics
Publisher Elsevier Science (Amsterdam, The Netherlands)
Reference URL View reference page