# Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8
## 1 Introduction
(2020) proposed a pavement damage image classification method based on a VGG-based shallow deep convolutional neural network model that realized the classification and recognition of five kinds of damage images of small-sample asphalt pavement transverse cracks, longitudinal cracks, looseness, cracks, and pits. [PERSON] et al. (2020) analyzed the vibration generated by tire-rolling movement, used machine learning techniques to analyze the signal, and used the self-organizing map (SOM) algorithm to classify and estimate the road surface. [PERSON] et al. (2020) proposed a pavement crack recognition method based on an improved Mask R-CNN model, which can completely identify, locate, and extract cracks with high precision. [PERSON] et al. (2020) proposed a top-down scheme for the detection and localization of potholes in pavement images to achieve accurate recognition of pothole images. [PERSON] et al. (2020) divided the road state into five categories: dry, wet, snow, ice, and water; the road state feature database was constructed to realize road state recognition based on a support vector machine according to the color and texture feature vectors. [PERSON] et al. (2020) proposed a vehicle sideslip angle estimation algorithm based on combined regression and classification artificial neural networks to estimate the sideslip angle and identify the road conditions of dry, wet, and icy road conditions. [PERSON] et al. (2020), casting pavement image segmentation for pothole detection as a problem of clustering multivariate features within mixed pixels, proposed a low-cost urban asphalt pavement pit detection method based on two-dimensional visual images. [PERSON] et al. (2020) extracted the time domain and the combined features of the time, frequency, and time-frequency domains of the original vibration signal and constructed an online classification model using a random forest algorithm to realize adaptive recognition of four different terrains. [PERSON] et al. (2020) proposed a real-time method for identifying road unevenness with serial acceleration signals and an unevenness-correlated adaptive suspension damping control; the long short-term memory network is used to identify the time domain features of the signal to realize the recognition and classification of road roughness. [PERSON] et al. (2020) used SVM, MLP, SGD, GNB, and extremely randomized trees techniques to estimate road types based on the brake pressure pulses of ABS. It can be seen from the previous that there are many studies on road recognition in the existing literature reports, most of which are on road diseases, and there are few studies on road pavement types. Even if there are studies involving pavement types, most of them are aimed at one variable, such as the dry and wet states of the same pavement, or the identification of pavements of 3-5 different materials (asphalt, cement, sand, tiles, slate) in the same state, and the research difficulty is low. In fact, the existing roads not only have changes in pavement materials or dry and wet conditions, but also have changes in weather conditions, pavement materials, damage degree, and other conditions. In particular, the coupling of various changing conditions leads to many kinds of real forms of pavement, and the difficulty of recognition is also greatly increased, which brings challenges to the outdoor application of wheeled robots. However, quickly and accurately identifying the type and comprehensive condition of the pavement ahead in a complex outdoor environment is a key prerequisite for the wheeled robot to predict whether it can pass safely. In order to promote the accurate recognition of environmental roads and the good application of indoor and outdoor environments by visual navigation robots, this paper will sample 21 kinds of road pavement types covering different weather conditions, different pavement materials, and different degrees of damage on the basis of building a pavement image capture system.