## 5 Conclusions  
This study demonstrates the technological innovation of the All Materials Locator (AML) system in the field of underground utility detection. Unlike conventional Ground-Penetrating Radar (GPR) systems, which are often constrained by limited detection depth and material sensitivity--particularly in identifying non-metallic pipelines such as PVC or PE--the AML system employs high-frequency signal analysis to detect subsurface density variations. This novel approach significantly enhances its versatility and detection capacity.  
Field evaluations focused on 3 m deep non-metallic pipelines, specifically polyethylene (PE) conduits. The AML system successfully detected all 10 measurement points along each target, with an average depth reading of approximately 2.524 m and a horizontal position averaging 5.75 m from the reference line. Deviation analysis across 32 sample sets confirmed high accuracy: horizontal and vertical deviations for PE pipelines averaged +0.0029 m and +0.0975 m, respectively, with standard deviations well within acceptable tolerance ranges (\(\pm\)0.30 m). The computed RMSE values--0.0616 m for position and 0.1229 m for depth--further reinforced the system's reliability and measurement consistency.  
In addition, histogram-based statistical assessments showed that the deviation distributions closely followed Gaussian behavior, indicating random error characteristics with no systematic bias. These results confirm that the AML system not only provides sub-decimeter accuracy but also offers robust reproducibility in controlled environments, even under the complex detection conditions associated with non-metallic utilities.  
The device's compact, user-friendly design and real-time feedback interface also enhanced field operability, positioning the AML as a practical and efficient alternative to traditional GPR- and EMI-based methods. These findings suggest that the AML system represents a forward-looking advancement in underground utility detection, with significant potential to improve excavation safety, reduce infrastructure damage, and support digital asset management.  
Future research should extend these findings by applying the AML system in diverse soil conditions and mixed-utility environments, and by exploring its integration with AI-assisted detection platforms, spatial databases, and digital twin systems to support next-generation smart infrastructure planning.  
Conceptualization, S.-J.L. and H.-S.Y.; methodology, S.-J.L.; software, S.-J.L.; validation, S.-J.L. and H.-S.Y.; formal analysis, S.-J.L.; investigation, S.-J.L.; resources, S.-J.L.; data curation, S.-J.L.; writing--original draft preparation, S.-J.L.; writing--review and editing, H.-S.Y.; visualization, S.-J.L.; supervision, H.-S.Y.; project administration, H.-S.Y.; funding acquisition, H.-S.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2021-NR059478).  
Not applicable.  
The original contributions presented in this study are included in this article, and further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.