And asphalt pavement is also damaged by human error such as rolling of vehicles, pavement materials, construction quality, and later maintenance level. IntroductionĪsphalt pavement is damaged by natural disasters such as long-term exposure to the sun, rain erosion, and natural weathering. The experimental results show that the detection accuracy of the YOLOv5l model is the highest, reaching 88.1%, and the detection time of the YOLOv5s model is the shortest, only 11.1 ms for each image. Then, for the dataset of crack pavement, YOLOv5 series models are used for training and testing. Firstly, the 3001 asphalt crack pavement images with the original size of pixels are collected using a digital camera and are randomly divided into three types according to the severity levels of low, medium, and high. This paper discusses the latest YOLOv5 series detection model for pavement crack detection and is to find out an effective training and detection method. As a result, those models have become possible for pavement crack detection.
Object detection based on the deep learning model has achieved good results in many fields. In the face of many types of pavement cracks, it is difficult to design a general feature extraction model to extract pavement crack features, which leads to the poor effect of the automatic detection model based on machine learning.
SONGKONG 4.1 CRACK MANUAL
According to the pavement distress identification manual proposed by the Federal Highway Administration (FHWA), these categories have three different types of cracks, such as fatigue, longitudinal crack, and transverse cracks. The detection model based on machine learning needs artificial design of pavement crack characteristics. Traditional artificial detection has some problems, such as low efficiency and missing detection.
If these cracks cannot be found and repaired in time, it will have a negative impact on the safe driving of vehicles. Severe weather and long-term driving of vehicles lead to various cracks on asphalt pavement.
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