# Research on Feature Identification and Trajectory Planning of Pavement Cracks
###### Abstract
Now, many scholars have proposed to train complex neural networks to obtain a model that can accurately identify pavement cracks and fit the trajectory [10; 11; 12]. However, although these methods could provide extremely fine fitting effect, the main drawback of learning-based approaches is that the build learning step is usually accompanied by a tricky manual labeling step, which does not provide fast and fully automatic analysis compared to image segmentation-based methods [2]. Most crack identification algorithms today have relatively single scene quality requirements for the processed image features. However, affected by objective factors such as light and shooting angle, the crack images identified by the machine inevitably have different contrasts, textures and shades. At present, there is still a lack of a systematic pavement crack detection and trajectory planning algorithm, which can balance high accuracy and fast execution efficiency, and is not limited to the complex manual labeling work of the dataset. In order to solve this problem, this paper proposes an intelligent pavement crack detection and repair algorithm. The algorithm establishes a targeted filtering model for different types of crack feature images, and realizes intelligent trajectory planning based on the Hough space transformation and K-Means clustering algorithm. Through a large number of experiments, we find that under the condition of fixed size, the crack images collected by the camera can be divided into two types according to the morphological characteristics of the main crack and the distribution characteristics of the noise, namely the high-density noise wide crack type and the low-density noise narrow crack type. According to this, the algorithm firstly performs global variance calculation on the binarized original crack image and accurately classifies two typical crack features based on the threshold discrimination method. For the high-density noise wide crack type images which tend to have wide main cracks and dense noise distribution, the algorithm uses the median filter and Otsu thresholding method, which greatly eliminates the image dense noise groups while retaining the main crack feature relatively completely. For the low-density noise narrow crack type images which tend to have narrow main cracks and scattered noise distribution, the algorithm accurately extracts the crack features through the eight-neighborhood maximum connected component extraction algorithm, thus avoiding the loss of narrow crack feature information caused by strong filtering. After crack feature extraction operation, the algorithm uses Hough space transformation and K-Means clustering algorithm to establish a mathematical model that can be combined with the nozzle size of the crack repair model for efficient trajectory planning. Finally, the robustness test of the algorithm is carried out on pavement image dataset containing various types of cracks, and experiments are carried out on the intelligent pavement crack detection and repair prototype to verify the accuracy and reliability of its planned trajectory.