Recent advances in deep learning have enabled automated ground‐penetrating radar (GPR) image analysis, particularly through two‐stage models like mask region‐based convolutional neural (Mask R‐CNN) and single‐stage models like you only look once (YOLO), which are two mainstream approaches for object detection and segmentation tasks. Despite their potential, the limited comparative analysis of these methods obscures the optimal model selection for practical field applications in tunnel lining inspection. This study addresses this gap by evaluating the performance of Mask R‐CNN and YOLOv8 for tunnel lining detection and segmentation in GPR images. Both models are trained using the labeled GPR image datasets for tunnel lining and evaluate their prediction accuracy and consistency based on the intersection over union (IoU) metric. The results show that Mask R‐CNN with ResNeXt backbone achieves superior segmentation accuracy with an average IoU of 0.973, while YOLOv8 attains an IoU of 0.894 with higher variability in prediction accuracy and occasional failures in detection. However, YOLOv8 offers faster processing times in terms of training and inference. It appears Mask R‐CNN still excels in accuracy in tunnel GPR lining detection, although recent advancements of the YOLOs often outperform the accuracy of the Mask R‐CNN in a few specific tasks. We also show that ResNeXt‐enhanced Mask R‐CNN further improves the accuracy of the traditional ResNet‐based Mask R‐CNN. The research finding offers useful insights into the trade‐offs between the accuracy, consistency, and computational efficiency of the two mainstream models for the tunnel lining identification task in GPR images. The finding is expected to offer guidance for the future selection and development of optimal deep learning‐based inspection models for practical field applications.

中文翻译：

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使用先进的单阶段和两阶段目标检测和分割模型从探地雷达图像中分割隧道衬砌

深度学习的最新进展使自动探地雷达 （GPR） 图像分析成为可能，特别是通过基于掩码区域的卷积神经 （Mask R-CNN） 等两阶段模型和单阶段模型，如你只看一次 （YOLO），这是对象检测和分割任务的两种主流方法。尽管它们具有潜力，但对这些方法的有限比较分析掩盖了隧道衬砌检查中实际现场应用的最佳模型选择。本研究通过评估 Mask R-CNN 和 YOLOv8 在 GPR 图像中隧道衬砌检测和分割的性能来解决这一差距。这两个模型都使用标记的 GPR 图像数据集进行隧道衬砌训练，并根据交并比 （IoU） 指标评估其预测准确性和一致性。结果表明，具有 ResNeXt 主干的 Mask R-CNN 实现了卓越的分割精度，平均 IoU 为 0.973，而 YOLOv8 的 IoU 为 0.894，预测精度具有更高的可变性和偶尔的检测失败。但是，YOLOv8 在训练和推理方面提供了更快的处理时间。尽管 YOLO 的最新进展在一些特定任务中经常超过掩码 R-CNN 的准确性，但 Mask R-CNN 在隧道 GPR 衬砌检测的准确性方面似乎仍然表现出色。我们还表明，ResNeXt 增强的掩码 R-CNN 进一步提高了传统基于 ResNet 的掩码 R-CNN 的准确性。研究结果为 GPR 图像中隧道衬砌识别任务的两个主流模型的准确性、一致性和计算效率之间的权衡提供了有用的见解。 这一发现有望为未来为实际现场应用选择和开发基于深度学习的最佳检测模型提供指导。