Ground penetrating radar multi-attribute fusion for multi-target detection of tunnel lining

doi:10.13474/j.cnki.11-2246.2025.1006

Abstract

Abstract: This paper addresses the issues of complex ground penetrating radar (GPR)image features and low accuracy in defect detection by proposing a multi-attribute fusion method for tunnel lining defect detection.By extracting instantaneous amplitude, instantaneous phase, and instantaneous frequency attributes of radar signals, combined with wave-particle duality theory to design a Wave module as the backbone network, a multi-modal feature fusion framework is constructed.The method employs a pyramid structure to extract low-level and high-level semantic features in layers, and introduces a lightweight MLP architecture to optimize network dynamics and computational efficiency.Experimental results demonstrate that the model fusing instantaneous attributes with the Wave module achieves a mean average precision (mAP)of 91.7%in cavity, loose, and steel detection tasks, an improvement of 3.8%compared to the baseline YOLOv8 model.Ablation experiments and comparative analysis verify the effectiveness of the multi-attribute fusion strategy and the Wave module in enhancing feature expression capability, providing a new approach for precise non-destructive detection of tunnel lining defects.

Key words: multi-attribute fusion, GPR, Wave module, pyramid architecture, non-destructive defection

CLC Number:

P237

ZHAO Liang, TANG Luyi, LIU Shipeng. Ground penetrating radar multi-attribute fusion for multi-target detection of tunnel lining[J]. Bulletin of Surveying and Mapping, 2025, 0(10): 30-35.

References

[1] XU Tianjia,YUAN Da,YANG Gexing,et al. FM-GAN: forward modeling network for generating approaching-reality B-scans of GPR pipelines with transformer tuning[J]. IEEE Transactions on Geoscience and Remote Sensing,2024,62:5102513. [2] LIU Zhen,GU Xingyu,WU Wenxiu,et al. GPR-based detection of internal cracks in asphalt pavement:a combination method of DeepAugment data and object detection[J]. Measurement,2022,197:111281. [3] LIN Y D,HSU Y C. Multihop cellular:a new architecture for wireless communications[C]//Proceedings of 19th Annual Joint Conference of the IEEE Computer and Communications Societies. Tel Aviv:IEEE,2000:1273-1282. [4] YANG Kaifu,LI Hui,KUANG Hulin,et al. An adaptive method for image dynamic range adjustment[J]. IEEE Transactions on Circuits and Systems for Video Technology,2019,29(3):640-652. [5] PACHORI R B. Time-frequency analysis techniques and their applications[M]. Boca Raton:CRC Press,2023. [6] MILLER S D,BANKERT R L,SOLBRIG J E,et al. A dynamic enhancement with background reduction algorithm:overview and application to satellite-based dust storm detection[J]. Journal of Geophysical Research:Atmospheres,2017,122(23):12,938-12,959. [7] JEON J,PARK J H,JEONG Y S. Dynamic analysis for IoT malware detection with convolution neural network model[J]. IEEE Access,2020,8:96899-96911. [8] LU Guoze,ZHAO Wenke,FORTE E,et al. Multi-frequency and multi-attribute GPR data fusion based on 2D wavelet transform[J]. Measurement,2020,166:108243. [9] CORBEANU R,MCMECHAN G A,SZERBIAK R B,et al. Prediction of 3D fluid permeability and mudstone distributions from ground-penetrating radar (GPR)attributes:Example from the Cretaceous Ferron Sandstone Member,east-central Utah[J]. 2002,67(5):1495-1504. [10] YUE Guanghua,DU Yuchuan,LIU Chenglong,et al. Road subsurface distress recognition method using multiattribute feature fusion with ground penetrating radar[J]. International Journal of Pavement Engineering,2023,24(2):1-13. [11] SOHAN M,SAI RAM T,RAMI REDDY C V. A review on YOLOv8 and its advancements[M]//Data Intelligence and Cognitive Informatics. Singapore:Springer Nature Singapore,2024:529-545. [12] DANESHGAR RAHBAR M,MOUSAVI MOJAB S Z. Enhanced U-Net with GridMask (EUGNet):a novel approach for robotic surgical tool segmentation[J]. Journal of Imaging,2023,9(12):282. [13] YUN S,HAN D,CHUN S,et al. CutMix:regularization strategy to train strong classifiers with localizable features[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul:IEEE,2019:6022-6031. [14] 张世文,周涛,王阳,等. 基于希尔伯特-黄变换的覆土层厚度动态探测[J]. 农业机械学报,2023,54(3):338-346. [15] FENG Zhipeng,LIN Xuefeng,ZUO M J. Joint amplitude and frequency demodulation analysis based on intrinsic time-scale decomposition for planetary gearbox fault diagnosis[J]. Mechanical Systems and Signal Processing,2016,72:223-240. [16] TANG Yehui,HAN Kai,GUO Jianyuan,et al. An image patch is a wave:phase-aware vision MLP[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans:IEEE,2022:10925-10934. [17] LIU Wei,ANGUELOV D,ERHAN D,et al. SSD:single shot MultiBox detector[M]//Computer Vision - ECCV 2016. Cham:Springer International Publishing,2016:21-37. [18] REN Shaoqing,HE Kaiming,GIRSHICK R,et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149. [19] WANG C Y,BOCHKOVSKIY A,LIAO H M. YOLOv7:trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver,BC:IEEE,2023:7464-7475.