Crack Identification for Bridge Structures Using an Unmanned Aerial Vehicle (UAV) Incorporating Image Geometric Correction

Crack assessment of bridge structures is essential for maintaining safe transportation infrastructure. Traditional crack detection by manual visual observation has drawbacks, as it expensive, time-consuming, and limited the height volume bridges. Recently, unmanned aerial vehicles (UAVs) with image processing have been used to address these limitations. However, cameras on UAVs will generally not be perpendicular surface during actual measurements; therefore, deviation in perspective angle can lead inaccuracies identification. In this work, we propose a robust straightforward method based geometric correction calibration algorithms issues. Four parallel laser emitters were installed UAV camera acquisition, laser-obtained images geometrically adjusted using four-point linear algorithm. After processing, object-to-image resolution was analyzed, information extracted. Compared lens imaging concept method, lasers showed greater precision width identification, measurement accuracy over 95%. This indicated that proposed identification system great potential