Abstract
Ground penetrating radar (GPR) is an effective tool for detecting internal defects in asphalt roads due to its non-destructive nature and high resolution. However, detecting defects in GPR images remains challenging, as existing models lack sufficient accuracy and are often complex and redundant. To address these issues, a lightweight real-time detection method based on ground-penetrating radar is proposed in this study. First, a field GPR image dataset of asphalt roads was collected and constructed. To address the limited defect sample data acquired by GPR, an efficient copy-and-paste augmentation method was employed. This method involved copying and pasting defect samples in GPR images while incorporating random scale jitter and position migration operations to generate a sufficient number of real defect samples. Second, the C2f-DSConv module and the SE attention mechanism were designed and introduced based on the YOLOv8 network to improve detection accuracy in the complex background environment of GPR images. Finally, a channel pruning strategy was used to prune the improved YOLOv8 network, reducing model complexity while maintaining detection accuracy. The final model achieves an average detection accuracy of 90.9% and a detection speed of 140.9 FPS. The results show that the proposed method combines both detection accuracy and real-time performance, further advancing the engineering application of internal defect detection in asphalt roads.
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Data will be made available on request. No datasets were generated or analysed during the current study.
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This project was supported by Natural Science Foundation of Qingdao Municipality (23-2-1-208-zyyd-jch).
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NL, JW, and ZL wrote the main manuscript text and prepared some of the figures and tables. WZ, KL undertook the data work. FZ prepared some of the figures and tables. All authors reviewed the manuscript.
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Li, N., Zhang, W., Liu, Z. et al. PDSE-YOLOv8: a lightweight detection method for internal defects in asphalt roads. SIViP 18, 8925–8936 (2024). https://doi.org/10.1007/s11760-024-03518-1
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DOI: https://doi.org/10.1007/s11760-024-03518-1