@Article{sdhm.2025.068987,
AUTHOR = {Hongcheng Zhao, Tong Yang , Yihui Hu, Fengxiang Guo},
TITLE = {Automatic Recognition Algorithm of Pavement Defects Based on S<sup><b>3</b></sup>M and SDI Modules Using UAV-Collected Road Images},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {20},
YEAR = {2026},
NUMBER = {1},
PAGES = {0--0},
URL = {http://www.techscience.com/sdhm/v20n1/65351},
ISSN = {1930-2991},
ABSTRACT = {With the rapid development of transportation infrastructure, ensuring road safety through timely and accurate highway inspection has become increasingly critical. Traditional manual inspection methods are not only time-consuming and labor-intensive, but they also struggle to provide consistent, high-precision detection and real-time monitoring of pavement surface defects. To overcome these limitations, we propose an Automatic Recognition of Pavement Defect (ARPD) algorithm, which leverages unmanned aerial vehicle (UAV)-based aerial imagery to automate the inspection process. The ARPD framework incorporates a backbone network based on the Selective State Space Model (S<sup>3</sup>M), which is designed to capture long-range temporal dependencies. This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery. Furthermore, a neck structure based on Semantics and Detail Infusion (SDI) is introduced to guide cross-scale feature fusion. The SDI module enhances the integration of low-level spatial details with high-level semantic cues, thereby improving feature expressiveness and defect localization accuracy. Experimental evaluations demonstrate that the ARPD algorithm achieves a mean average precision (mAP) of 86.1% on a custom-labeled pavement defect dataset, outperforming the state-of-the-art YOLOv11 segmentation model. The algorithm also maintains strong generalization ability on public datasets. These results confirm that ARPD is well-suited for diverse real-world applications in intelligent, large-scale highway defect monitoring and maintenance planning.},
DOI = {10.32604/sdhm.2025.068987}
}