Yamaguchi 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. Ouma 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. Tedeschi et al. [
4] developed an OpenC-based automatic pavement damage recognition system that achieved the recognition of three common pavement distresses: potholes, longitudinal or transversal cracks, and fatigue cracks. Wu et al. [
5,
6] 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. Maeda et al. [
7] 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. Zhao Jian et al. [
8] 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. Yang et al. [
9] proposed a feature pyramid, hierarchical boosting network, and average intersection over union to achieve pavement crack detection. Feng et al. [
10] 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. Wang et al. [
11] 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. Liu et al. [
12] 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. Shi et al. [
13] used semi-supervised learning to carry out research on robot terrain classification based on vibration, which significantly improved classification accuracy. Chen et al. [
14] 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. Dimastrogiovanni et al. [
15] 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. By improving the AlexNet model, Wang et al. [
16] constructed a common road-type image perception recognition model with higher network training speed and road image recognition accuracy. Qin et al. [
17] developed a road classification output model through signal time–frequency processing and PNN with the object of steep mass acceleration, unsprung mass acceleration, and rattle space. Wang et al. [
18] constructed a pavement classification model based on structural reparameterization and adaptive attention to realize the rapid and accurate identification of complex pavements, such as asphalt, cement, ice and snow, sand, flower brick, slate, wet, and slippery. Wang et al. [
19] combined convolutional neural networks with support vector machines to achieve RGB image recognition and classification of six common terrains: grass, mud, sand, asphalt, gravel, and hydrops. Xu et al. [
20] proposed a road surface apparent damage image recognition method based on historical information, introduced a mechanism of using historical information to create initial constraints for damage identification, trained the VGG-16 network to extract damage features, and finally used the improved genetic algorithm to achieve a significant increase in recognition speed under the condition of ensuring recognition accuracy. Chen et al. [
21] proposed a thermal–RGB fusion image-based pavement damage detection model to achieve accurate detection of pavement damage. Kou et al. [
22] established the BAS-BP road recognition model and constructed an adaptive fuzzy control of electromagnetic hybrid suspension based on road recognition. Dewangan et al. [
23] constructed a road classification network model based on a convolutional neural network and realized the recognition of five main types of roads: curvy, dry, ice, rough, and wet. Zhang et al. [
24] proposed a forward road adhesion coefficient prediction method based on image recognition. Road segmentation and road type identification are realized, and the forward road adhesion coefficient is obtained using DeeplabV3+, a semantic segmentation network, and a MobilNetV2 lightweight convolutional neural network. Yousefzadeh et al. [
25] measured surface roughness using accelerometers coupled with high-speed distance sensors and other methods, combined with an artificial neural network, to achieve road profile estimation. Du et al. [
26] proposed a road profile elevation inversion and roughness estimation method based on vehicle vibration response signals. The international roughness index is solved by using the vehicle body vibration response signal as the measured value, and the road roughness is accurately evaluated by combining multi-vehicle collaborative estimation. Bai et al. [
27] proposed a deep neural network based on vibration multilayer perception to realize terrain classification and recognition, and completed the terrain classification and recognition of planetary detectors. Yang et al. [
28] proposed a method based on semantic segmentation to realize intelligent detection of asphalt pavement cracks in highways and other scenarios. Conducting experiments on two different road types, dry and wet, Šabanovič et al. [
29] studied road type, condition recognition, and friction coefficient estimation based on a deep neural network (DNN) and a video image sensor. Li et al. [
30] collected laser radar and image data for spatiotemporal matching, and proposed a three-dimensional shape and size extraction method for vehicles based on road space division, road segmentation, and laser point cloud gathering, as well as a vehicle labeling method covering steps such as target filtering and classification, identification difficulty division, three-dimensional bounding box calibration, and label information supplement. Cheng et al. [
31] proposed a new DyVTC learning framework for robot terrain classification based on vibration. Chen et al. [
32] 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. Andrades et al. [
33] 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. Xiao et al. [
34] 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. Yousaf et al. [
35] proposed a top-down scheme for the detection and localization of potholes in pavement images to achieve accurate recognition of pothole images. Zhao et al. [
36] 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. Bonfitto et al. [
37] 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. Ouma et al. [
38], 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. Wang et al. [
39] 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. Liang et al. [
40] 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. Yiğit et al. [
41] 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. By constructing a pavement pattern recognition model based on a classical neural network and the YOLO v8 target detection model, the problem of road pattern recognition is studied. On this basis, by studying the improvement and optimization method of the YOLOv8n pavement pattern recognition model, the best recognition model for improving the accuracy of pavement pattern recognition is explored.
(1) Through the coupling of weather conditions, pavement materials, damage degree, and other pavement conditions, 21 kinds of complex pavement patterns are constructed, which provides effective and reliable experimental sample data for the effective recognition of pavement patterns under difficult conditions.
(2) Research on pavement recognition of three deep learning algorithms and two Yolo target detection algorithms is carried out. Through comparative analysis, Yolov8n is determined to be the basic framework model for pavement pattern recognition, which provides a reliable foundation model for accurate pavement detection and model improvement research.
(3) The improvement of the YOLOv8n road pattern recognition model was carried out based on the C2f-ODConv module, the AWD adaptive weight downsampling module, the EMA attention mechanism, and three-module collaboration, respectively, and the best improved YOLOv8 recognition model to realize accurate pavement detection was determined.