A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Identification, 3D-Reconstruction, and Classification of Dangerous Road Cracks
Tekijät: Sghaier Souhir, Krichen Moez, Ben Dhaou Imed, Elmannai Hela, Alkanhel Reem
Kustantaja: MDPI
Julkaisuvuosi: 2023
Journal: Sensors
Tietokannassa oleva lehden nimi: SENSORS
Lehden akronyymi: SENSORS-BASEL
Artikkelin numero: 3578
Vuosikerta: 23
Numero: 7
Sivujen määrä: 19
eISSN: 1424-8220
DOI: https://doi.org/10.3390/s23073578
Verkko-osoite: https://doi.org/10.3390/s23073578
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
