## 4 Conclusions  
To address the issue of traditional highway sensing devices being primarily concentrated at toll stations and ramps while neglecting the intermediate road sections, this paper proposes a novel highway surface monitoring method that combines robotics technology with edge computing. We utilize an improved YOLOv5s neural network algorithm for real-time detection of road debris and surface damage on highways. By incorporating an advanced convolution method, GSConv, the model becomes more lightweight without significantly sacrificing feature extraction capability, making it more suitable for operation on edge computing platforms.  
Furthermore, we employ a label allocation strategy based on optimal transport theory to enhance the model's accuracy and robustness in dealing with complex road anomaly scenarios. We also constructed an HSRD dataset encompassing a variety of complex road conditions, including debris and surface damage, to support our research. Compared to the original model, our modified version shows a 21% reduction in parameters and an 18% decrease in floating-point operations, with a 2% increase in mean accuracy and a 1.3 times improvement in frames per second (FPS). This indicates that our algorithm maintains its lightweight nature while achieving higher detection accuracy and better real-time performance.  
Finally, we deployed this algorithm on an intelligent road patrol platform, effectively monitoring highway surface conditions. When the camera captures road defects and debris on highways, our enhanced algorithm deployed on smart inspection robots will complete the detection of abnormalities in the scene in approximately 70 milliseconds. Subsequently, utilizing networking devices, it will transmit key information such as location and category through the internet to be displayed on a cloud interface. Provided the network transmission is smooth, the entire process from detection to display on the cloud interface will be completed within one second. Through this comprehensive approach, we are able to monitor highway surface conditions more thoroughly and accurately, contributing to road safety and improved traffic efficiency. The goal of future work is to enhance the diversity of the dataset, for example, by incorporating data on tires discarded due to blowouts, thereby increasing the number of categories recognizable by the target detection model. Simultaneously, efforts will also be directed toward improving the adaptability of the intelligent inspection robot to various types of guard trails, aiming to enhance its practicality in different scenarios.  
Figure 16: Cloud platform interface.  
**Author Contributions:** Conceptualization, B.S. and J.S.; methodology, J.S.; software, J.S.; validation, J.S.; formal analysis, Y.Z.; investigation, B.S.; resources, B.S.; data curation, Y.Z.; writing--original draft preparation, J.S.; writing--review and editing, Y.Z. and B.S.; visualization, J.S.; supervision, Y.Z.; project administration, B.S.; funding acquisition, B.S. All authors have read and agreed to the published version of the manuscript.  
This project was supported by National Natural Science Foundation of China (52237008, 52107176).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to current proprietary.  
The authors are grateful to the editors and anonymous reviewers for their informative suggestions.  
The authors declare no conflicts of interest.