Application of YOLOv8 Model in Recognition of Hidden Diseases in Asphalt Pavement
Gound-penetrating radar technology has been widely applied in detecting hidden pavement diseases.However,conventional radar image recognition mainly relies on labor,without a standardized process,has a certain subjectivity and low efficiency,and is not conducive to promoting and applying ground-penetrating radar technology.This paper used the YOLOv8 deep learning model to study and recognize asphalt pavement diseases,and the study results showed that the average accuracy and recall rate of the trained YOLOv8 model reached 92.45％and 89.25％,respectively,and could be used in recognizing hidden pavement diseases.The trained model was applied in a detection project of the expressway pavement and three types of diseases were recognized,including cracks,poor cohesiveness between layers,and loose.The research method and results have strong practical application value for the detection of hidden diseases in asphalt pavements.
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