College of Information and Mechanical Electrical Engineering,Ningde Normal University
Fujian Natural Science Foundation Project (2023J011090); Ningde Normal University Science and Technology Research Project for Middle and Young Teachers (2020Q109).
As one of the important criteria for bridge health detection, cracks can reflect the damage status of bridge structures. In response to the shortcomings of traditional bridge crack detection methods such as low efficiency, subjectivity, and time-consuming, this paper proposes an improved Otsu bridge crack detection method. The bias parameter and background tendency coefficient are added before the target variance, and the peak information is obtained by using the gray value gradient cumulant, so as to ensure that the threshold is always on the left side of the peak in the case of a single peak, and then the threshold is adjusted appropriately by the gray value cumulant to finally achieve adaptive bridge crack detection.The experimental results show that the improved algorithm in this paper can effectively detect bridge cracks, and compared to improved algorithms such as EW, WOV, VE, etc., the misclassification value obtained by the improved algorithm in this paper is closer to 0, and the defect detection rate is closer to 1, resulting in better detection performance.