Year: 2025
Type: article
Abstract: Concrete bridge cracks are critical indicators for maintenance planning. Traditional visual inspections are often subjective, labor-intensive, and time-consuming, requiring close-range access by inspectors. In contrast, UAV-based remote sensing, combined with advanced image processing, offers a more efficient and accurate solution. This study proposes an enhanced crack detection method combining Laplacian of Gaussian (LoG) filtering and Otsu thresholding to improve segmentation accuracy through background noise suppression. The proposed approach extracts key crack characteristics—including area, length, centroid, and main direction—enabling precise damage assessment. Experimental validation on a real bridge dataset demonstrates significant improvements in detection accuracy. The method provides a reliable tool for automated structural health monitoring, supporting data-driven maintenance decisions.
Source: Infrastructures
Authors: Yanli Chen, Hongze Li, Hang Zhu, Tianlong Ren, Zhe Cao
Institution: Jilin University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4411739121