# Research on Feature Identification and Trajectory Planning of Pavement Cracks
###### Abstract  
As the most common method to detect pavement cracks, manual detection has uncontrollable factors such as low efficiency, inconsistent standards and easy to be interfered with by external forces, so it is not suitable for pavement crack detection in today's intricate traffic network. In order to improve the efficiency of pavement repair and reduce the labor cost of the repair process, this paper proposes an intelligent pavement crack detection and repair algorithm. The algorithm uses image numerical parameters to classify cracks with different geometric features and extracts texture geometric features of various types of cracks based on different filtering strategies. It solves the problem that traditional single filtering algorithms are difficult to extract features according to the different characteristics of the collected image, which leads to the loss of information. Finally, the algorithm establishes a mathematical model for efficient trajectory planning combined with the nozzle size of the crack-repairing machine. In this paper, the robustness and efficiency test of the algorithm is carried out on the pavement image dataset with various types of cracks, and the experiment is carried out on the intelligent pavement crack detection and repair prototype, which verifies the accuracy and reliability of the planned trajectory. crack detection; pavement repair; computer vision; intelligent trajectory planning; categorical feature extraction +
Footnote †: journal: Applied  
manipulation and preprocessing to extract the crack image of pavement damage and calculate the crack width. The researchers also used other methods based on autocorrelation screening, such as two-dimensional pavement image crack detection method based on the weighted neighborhood pixel segmentation algorithm, and Gaussian cumulative density function(CDF) as an adaptive threshold to overcome the disadvantage of fixed threshold in noisy environment [3]. Aiming at the problems of low crack image recognition rate and difficult classification, [PERSON] et al. [4] proposed a crack recognition method based on image enhancement and image segmentation. [PERSON] et al. [5] proposed a crack detection method based on the Otsu thresholding method and image segmentation, which does not need to know the morphological characteristics of cracks in advance, but uses the spatial distribution characteristics of crack pixels to analyze the image. Nevertheless, for complex and diverse pavement crack detection work, such traditional detection algorithms based on image segmentation algorithms have problems of low efficiency and poor robustness [6]. Machine learning methods can efficiently and accurately classify images through image spatial distribution, color tone, pixels and other features before specific crack identification, so are more and more frequently used to solve pavement crack detection problems. [PERSON] et al. [1] proposed an improved Otsu-Canny edge detection algorithm, and the crack detection based on the adaptive multi-resolution threshold technology improved the accuracy of crack extraction. [PERSON] et al. [7] proposed a method for identifying cracks in asphalt pavement based on Support Vector Machine and Otsu thresholding method. As an efficient feature extraction technology, Hough transformation is also very helpful to solve the problem of crack identification. By comparing a variety of edge detection, [PERSON] et al. [8] proposed a crack detection method based on Canny-Prewitt operator and Hough transformation, and proved that the algorithm has the best robustness and efficiency through experiments. [PERSON] [9] proposed a road trajectory fitting method based on Hough space transformation and the K-means clustering algorithm, which greatly reduces the number of feature points while fitting the trajectory efficiently. Neural network is the core of trainable algorithm for crack detection, which has advantages over threshold-based techniques and morphological tools.