Identification, 3D-Reconstruction, and Classification of Dangerous Road Cracks

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.

Automated summary

Advances in technology have allowed the development of automated inspection systems for road cracks, but the identification process is still in its early stages due to challenges in obtaining pavement photographs and the small size of cracks. The existence of cracks reduces the value of infrastructure, making the estimation of fracture severity crucial. This work aims to create an efficient automated system for crack identification, extraction, and 3D reconstruction to prevent traffic deaths and improve road safety.