# Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8
## 1 Introduction  
Road type and pavement information are prior information for various tasks, such as vehicle control and path planning [1]. The wheeled robot's recognition results for the road state directly affect its decision-making results. Therefore, the autonomous recognition capability of the wheeled robot for the road state determines the running performance and control capability of the wheeled robot. Accurate recognition of the road state is the premise and necessary condition for operational decision making and real-time path planning adjustment. For a vision-guided wheeled robot, it is particularly important to use visual information for road previews and accurate recognition of the road state. [PERSON] et al. [2] increased background classification and used convolutional neural networks and a self-organizing map to detect road cracks, which improved the accuracy of crack detection and reduced false detection. [PERSON] et al. [3] proposed a triple transform method based on wavelet morphology that used an RGB camera for the detection of initial linear cracks in asphalt pavements, providing a reliable method for the formal identification of early linear structural damage to asphalt pavements. [PERSON] et al. [4] developed an OpenC-based automatic pavement damage recognition systemthat achieved the recognition of three common pavement distresses: potholes, longitudinal or transversal cracks, and fatigue cracks. [PERSON] et al. (2016, 2017) proposed a lidar-based method of geometric parameter identification and model reconstruction for uneven roads and abnormal pavements. The semantic segmentation method of uneven features of non-paving roads was studied to achieve the classification of uneven features with different sizes and elevation differences. [PERSON] et al. (2017) used a smartphone to take a large number of road damage images, and used a convolutional neural network to construct a damage detection model to achieve accurate identification of eight types of damaged pavements. [PERSON] et al. (2018) measured the driving data of an experimental vehicle under four kinds of road conditions: compacted soil road, sandy land, good asphalt road, and icy or snowy road. A high-dimensional random forest surface recognition model was established, and the SHAP interpretation method was used to extract the correlation between each feature and road type. Based on this, a reduced-dimensional random forest pavement classification was designed to accurately identify the real vehicle driving pavement type. [PERSON] et al. (2019) proposed a feature pyramid, hierarchical boosting network, and average intersection over union to achieve pavement crack detection. [PERSON] et al. (2019) proposed a small-sample bridge pavement crack segmentation model based on a multi-scale feature fusion network, which improved the accuracy and performance of bridge pavement crack segmentation. [PERSON] et al. (2019) proposed a vehicle road terrain classification method based on acceleration, enabling the road contour to be calculated by vehicle speed and a one-quarter-dynamic model. [PERSON] et al. (2019) proposed a multidimensional feature fusion and recognition technology for off-road pavements that integrates acceleration features and image-depth features to detect passable areas of off-road pavements. [PERSON] et al. (2019) used semi-supervised learning to carry out research on robot terrain classification based on vibration, which significantly improved classification accuracy. [PERSON] et al. (2019) used two-dimensional and three-dimensional images of the road surface as network input, and used deep learning based on a multi-branch framework to realize the segmentation of road strip repair, potholes, looseness, bridge joints, and the recognition of cracks and block repair. [PERSON] et al. (2019) studied a planetary terrain detection method based on a support vector machine by estimating the motion state of the probe vehicle and the physical variables related to the interaction between the vehicle and the environment.