Abstract
Severe weather and long-term driving of vehicles lead to various cracks on asphalt pavement. If these cracks cannot be found and repaired in time, it will have a negative impact on the safe driving of vehicles. Traditional artificial detection has some problems, such as low efficiency and missing detection. The detection model based on machine learning needs artificial design of pavement crack characteristics. 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. 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. Object detection based on the deep learning model has achieved good results in many fields. As a result, those models have become possible for pavement crack detection. This paper discusses the latest YOLOv5 series detection model for pavement crack detection and is to find out an effective training and detection method. Firstly, the 3001 asphalt crack pavement images with the original size of 2976 × 3978 pixels are collected using a digital camera and are randomly divided into three types according to the severity levels of low, medium, and high. Then, for the dataset of crack pavement, YOLOv5 series models are used for training and testing. 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.
Highlights
Asphalt pavement is damaged by natural disasters such as long-term exposure to the sun, rain erosion, and natural weathering
To solve the problems existing in the above detection methods, Chen and Jahanshahi [11] improved the traditional convolutional neural network and proposed a crack detection algorithm combining the convolutional neural network with naive Bayesian (NB-CNN) data fusion
Combined with the current best object detection model, and applying it to the detection of crack pavement, it will greatly improve the efficiency of pavement detection
Summary
Asphalt pavement is damaged by natural disasters such as long-term exposure to the sun, rain erosion, and natural weathering. To solve the problems existing in the above detection methods, Chen and Jahanshahi [11] improved the traditional convolutional neural network and proposed a crack detection algorithm combining the convolutional neural network with naive Bayesian (NB-CNN) data fusion In this method, the convolution neural network and naive Bayes are integrated, which greatly increases the complexity of the model and the number of parameters and makes the model more difficult to train. FCN can collect features of different layers, where the shallow features can concentrate on spatial information and deep features can locate the objects, and fuse different features to achieve a damage prediction map On this basis, many detection models are proposed for crack pavement detection [14,15,16,17].
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