Automatic Recognition Algorithm of Pavement Defects Based on S3M and SDI Modules Using UAV-Collected Road Images
Hongcheng Zhao1, Tong Yang 2, Yihui Hu2, Fengxiang Guo2,*
1 Yunnan Transportation Science Research Institute Co., Ltd., Kunming, 650200, China
2 Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, 650500, China
* Corresponding Author: Fengxiang Guo. Email: 
(This article belongs to the Special Issue: AI-Enhanced Low-Altitude Technology Applications in Structural Integrity Evaluation and Safety Management of Transportation Infrastructure Systems)
Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2025.068987
Received 11 June 2025; Accepted 18 July 2025; Published online 12 November 2025
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
3M), 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.
Keywords
Pavement defects; state space model; UAV; detection algorithm; image processing
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