# Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8
## 6 Conclusions  
In order to realize the visual navigation robot's accurate recognition of and navigation control in the environment, this paper carries out research on the image recognition of pavement patterns. By building a data acquisition test system, 21 different patterns of pavement images, such as different weather conditions, pavement materials, and degrees of pavement damage, are collected and the pavement environment dataset is formed. The classical neural network algorithm Resnet 18 and the emerging image target detection algorithm YOLOv8 are used to carry out pavement pattern recognition research. On this basis, in order to further improve recognition performance, the pavement pattern recognition model based on YOLOv8 is improved in various ways, and the pavement pattern recognition model based on improved YOLOv8 is tested for recognition performance. The following conclusions are obtained:  
(1) The experimental system of pavement image shooting is set up, and 3178 pavement images of three different weather conditions, such as dry pavement, wet pavement, and waterlogged pavement, five different pavement materials, such as asphalt road, concrete road, soil road, sand road, and masonry road, and three different damage degrees, such as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Improved Point** & **Imagesize** & **Parameters** & **GFLOPS** & **Toplacc-val** \\ \hline YOLO v8n-C & YOLO v8n + C2f-ODConv & 224 & 1,146,273 & 0.10 & 0.927 \\ \hline YOLO v8n-A & YOLO v8n + AWD & 224 & 832,437 & 0.17 & 0.921 \\ \hline YOLO v8n-E & Yolovv8n + EMA & 224 & 1,121,685 & 0.19 & 0.921 \\ \hline YOLO v8n-CAE & YOLO v8n + C2f-ODConv + AWD + EMA & 224 & 877,761 & 0.09 & 0.932 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The road pattern recognition results based on improved YOLO v8. good pavement, slightly damaged pavement, and heavy damaged pavement, are jointly collected to form 21 different modes of pavement image datasets. (2) Based on the study of the network structure mechanism of Resnet 18, a pavement pattern recognition model based on Resnet 18 is constructed, 2550 photos are randomly selected as the training set, and the remaining 628 photos are used as the test set. The accuracy of pavement pattern recognition is found to be 88.4%. (3) On the basis of studying the mechanism of the YOLO v8 target detection model, a pavement pattern recognition model based on YOLOv8n is constructed, and the recognition model is trained according to different conditions of pavement images. The test recognition accuracy of 21 groups of pavement images with different modes is 91.5%. Compared with the pavement pattern recognition model based on Resnet 18, the recognition accuracy is improved by 3.1%. Recognition accuracy and response speed are both improved, and the recognition effect is better. (4) In order to further improve the recognition ability of the pavement pattern recognition model based on YOLOv8n, the C2f-ODConv module, the AWD adaptive weight downsampling module, and the EMA attention mechanism, respectively, are introduced to optimize, replace, or supplement some modules of the pavement pattern recognition model based on YOLOv8n. Three improved YOLO v8 pavement recognition models based on single module changes are obtained, namely an improved YOLO v8 road surface recognition model based on the C2f-ODConv module, an improved YOLO v8 road surface recognition model based on the AWD adaptive weight downsampling module, and an improved YOLO v8 road surface recognition model based on the EMA attention mechanism. And an improved YOLO v8 road surface recognition model based on multimodule coordination optimized by multiple modules is obtained. The pavement pattern recognition results show that the accuracy of the four optimized pavement pattern recognition models reaches more than 92%. Compared with the pavement pattern recognition model based on YOLO v8n, recognition accuracy is improved.