# High-Speed Surface Property Recognition with a 140 GHz Frequency
## 7 Conclusions  
In the field of integrated sensing and communication, environmental sensing is becoming as crucial as data transfer. This work proposes an approach in enhancing high-speed scanning capabilities for surface roughness recognition using terahertz frequency. Combining a deep learning framework, this method employs authentic high-speed sampling data to build an automated platform capable of capturing high-fidelity data in scenarios that closely emulate real-world dynamics. The manufacturing and 3D laser characterization of a spectrum of surfaces have facilitated the creation of a rich dataset, important for the training of our specialized neural network. We have aligned our emulated environments with actual laboratory and urban conditions, customizing our model to overcome three typical high-speed challenges: random occlusion, high-speed sparse data, and narrow-angle window observations. Our experimental results have confirmed the effectiveness of this method, by achieving an \(F1\) score of over 95% in scenarios with _random occlusion_. This indicates exceptionally high accuracy within our current dataset, especially in relatively slower situations. Additionally, our approach consistently maintains \(F1\) scores above 90%, 80%, and 70% at emulated vehicular cruising speeds (40 km/h), urban vehicle speed limits (60 km/h), and drone flight speeds (80 km/h), respectively. Notably, we have achieved a 71% \(F1\) score within a 120\({}^{\circ}\) range using only 10 randomly positioned scattering data points, highlighting the success of our method in challenging conditions. Moreover, we have identified window positions within a 60\({}^{\circ}\) angular range that consistently maintain high speeds, resulting in \(F1\) scores above 90% with 20 randomly positioned scattering data points. This approach demonstrates exceptional accuracy, especially in medium- to low-speed scenarios and constrained angular windows. Even under extreme conditions, such as very high speeds (e.g., 80 km/h), it maintains a significant degree of reliability. This method may not only prompt the application of terahertz frequency band in surface recognition, but also enriches the broader context of sensing-communication integration, contributing to the evolution of intelligent, high-speed recognition systems. Figure 5: Window positions we attempted under angle-restricted conditions. Each window covers a 60\({}^{\circ}\) sector, with two red arrows representing the incident and outgoing waves. Each red serial number corresponds to the Idx. index number. For future works, we have two primary objectives: 1. We intend to deploy the methods presented in this paper onto actual vehicles, conducting real-world field experiments to validate and refine our approach. Our ultimate aim is to implement practical and effective scanning technologies with real-world applicability. 2. We plan to explore scenarios involving more complex scattering angles, such as surfaces with inclined incidence, Rx and Tx operating in different planes, to support applications in diverse and complex real-world environments. Methodology, J.L. and M.W. ; Software, J.L. and Y.Q. ; Validation, G.L. and C.Z. ; Formal analysis, J.M. ; Investigation, J.L. and J.M. ; Resources, J.M. ; Data curation, J.L. and D.L. ; Writing--original draft, J.L. ; Writing--review & editing, J.L. ; Visualization, J.M. ; Supervision, J.M. ; Project administration, J.M. ; Funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (62071046) and the Science and Technology Innovation Program of Beijing Institute of Technology (2022 CX01023). This study did not involve any experiments on animals or humans. Informed Consent Statement: Informed consent was not required for this study as it did not involve human participants. Data Availability Statement: Data available within the article and available on request from the corresponding authors. The authors declare no conflict of interest.