A1 Refereed original research article in a scientific journal
Identification, 3D-Reconstruction, and Classification of Dangerous Road Cracks
Authors: Sghaier Souhir, Krichen Moez, Ben Dhaou Imed, Elmannai Hela, Alkanhel Reem
Publisher: MDPI
Publication year: 2023
Journal: Sensors
Journal name in source: SENSORS
Journal acronym: SENSORS-BASEL
Article number: 3578
Volume: 23
Issue: 7
Number of pages: 19
eISSN: 1424-8220
DOI: https://doi.org/10.3390/s23073578
Web address : https://doi.org/10.3390/s23073578
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/179553610
Advances in semiconductor technology and wireless sensor networks have permitted the development of automated inspection at diverse scales (machine, human, infrastructure, environment, etc.). However, automated identification of road cracks is still in its early stages. This is largely owing to the difficulty obtaining pavement photographs and the tiny size of flaws (cracks). The existence of pavement cracks and potholes reduces the value of the infrastructure, thus the severity of the fracture must be estimated. Annually, operators in many nations must audit thousands of kilometers of road to locate this degradation. This procedure is costly, sluggish, and produces fairly subjective results. The goal of this work is to create an efficient automated system for crack identification, extraction, and 3D reconstruction. The creation of crack-free roads is critical to preventing traffic deaths and saving lives. The proposed method consists of five major stages: detection of flaws after processing the input picture with the Gaussian filter, contrast adjustment, and ultimately, threshold-based segmentation. We created a database of road cracks to assess the efficacy of our proposed method. The result obtained are commendable and outperform previous state-of-the-art studies.
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