基于移动激光扫描的地铁隧道渗漏水定位及快速检测方法

doi:10.11947/j.AGCS.2024.20230438

摘要/Abstract

摘要：

渗漏水是地铁隧道最主要的病害之一，也会导致其他结构病害，开展地铁隧道渗漏水病害检测方法研究具有重要意义。本文聚焦于地铁隧道渗漏水问题，提出了一种基于移动激光扫描点云数据的渗漏水定位及检测方法。首先，结合移动激光扫描检测方法，开展了隧道精确定位方法研究。然后，对YOLOv7模型进行了改进，引入了Conv NeXt网络和CBAM模块以使模型更好地捕获多尺度、多抽象级别的特征，增强对渗漏水关键特征的关注；使用GIoU Loss损失函数，使模型能够更好地处理不完整渗漏水框；预测时使用Soft-NMS加权平均的方法，保留更多的边界框，从而提高检测精度。结合在重庆地铁获取的激光扫描数据构建的盾构法和矿山法隧道渗漏水数据集，验证了本文方法的高效性和稳健性。消融试验表明，本文方法相较于基线模型在不同数据集上均取得了显著的性能提升，在盾构法数据集中，准确率P提升了8.1%，召回率R提升了4%；在矿山法数据集中，准确率P提升了8.6%，召回率R提升了6.8%。同时，与主流目标检测算法，如Faster RCNN（Swin）、Faster RCNN（Conv NeXt）、YOLOv8对比，本文方法在精度和速度方面都表现出优势。最后，本文展示了部分隧道渗漏水的定位与检测结果，以验证本文方法的实用性。

关键词: 激光点云, 隧道定位, 盾构法, 矿山法, 渗漏水检测, 注意力机制

Abstract:

Water leakage is one of the most important diseases in subway tunnels and can also cause other structural diseases. It is of great significance to carry out research on detection methods of water leakage diseases in subway tunnels. This paper focuses on the problem of water leakage in subway tunnels and proposes a water leakage location and detection method based on mobile laser scanning point cloud data. First, combined with the mobile laser scanning detection method, research on the precise positioning method of tunnels was carried out. Then, the YOLOv7 model was improved, and the ConvNeXt network and CBAM module were introduced to enable the model to better capture multi-scale, multi-abstraction level features and enhance attention to the key features of seepage water. The GIoU Loss function was used to make the model can better handle incomplete leaky water boxes. It uses the Soft-NMS weighted average method when predicting to retain more bounding boxes, thereby improving detection accuracy. The efficiency and robustness of the method in this paper are verified by combining the shield method and mine method tunnel leakage data sets constructed with laser scanning data obtained in Chongqing metro. The ablation test shows that compared with the baseline model, the method in this paper has achieved significant performance improvements on different data sets. In the shield method data set, the precision rate P is increased by 8.1%, and the recall rate R is increased by 4%. In the mining law data set, the precision rate P increased by 8.6%, and the recall rate R increased by 6.8%. At the same time, compared with the mainstream target detection algorithms faster RCNN (Swin), faster RCNN (ConvNeXt), and YOLOv8, this method shows advantages in both accuracy and speed. Finally, this paper shows the location and detection results of water leakage in some tunnels to verify the practicability of this method.

Key words: laser point cloud, tunnel localization, shield method, mine method, leakage detection, attention mechanism

中图分类号:

P258

纪长琦, 郭肇捷, 孙海丽, 钟若飞. 基于移动激光扫描的地铁隧道渗漏水定位及快速检测方法[J]. 测绘学报, 2024, 53(6): 1236-1250.

Changqi JI, Zhaojie GUO, Haili SUN, Ruofei ZHONG. Location and rapid detection method of water leakage in subway tunnels based on mobile laser scanning[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1236-1250.

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数据集名称	训练集	测试集	总数
盾构隧道数据集	258	64	322
盾构隧道扩充数据集	1290	320	1610
矿山法隧道数据集	255	63	318
矿山法隧道扩充数据集	1275	315	1580

数据集	损失函数	总标记	TP	FP	FN	P	R	m AP0.5	F₁值
盾构法	CIoU	375	303	80	72	0.791	0.808	0.807	0.799
	DIoU	375	283	49	92	0.852	0.755	0.802	0.801
	GIoU	375	312	83	63	0.790	0.832	0.825	0.810
矿山法	CIoU	398	302	74	96	0.803	0.759	0.815	0.780
	DIoU	398	300	97	98	0.756	0.754	0.802	0.755
	GIoU	398	323	71	75	0.820	0.812	0.848	0.816

试验组	CNEB	CBAM	GIoU	soft NMS
G1	×	×	×	×
G2	√	×	×	×
G3	×	√	×	×
G4	×	×	√	×
G5	×	×	×	√
G6	√	√	×	×
G7	√	√	√	×
G8	√	√	√	√

试验组	总标记	TP	FP	FN	P	R	m AP0.5	F₁值
G1	375	303	80	72	0.791	0.808	0.807	0.799
G2	375	316	103	59	0.753	0.843	0.82	0.795
G3	375	293	56	82	0.84	0.781	0.837	0.809
G4	375	312	83	63	0.790	0.832	0.825	0.810
G5	375	304	63	71	0.828	0.811	0.799	0.819
G6	375	319	73	56	0.814	0.851	0.852	0.832
G7	375	318	53	57	0.857	0.848	0.868	0.853
G8	375	320	47	55	0.872	0.853	0.865	0.862

试验组	总标记	TP	FP	FN	P	R	m AP0.5	F₁值
G1	398	302	74	96	0.803	0.759	0.815	0.78
G2	398	329	91	69	0.783	0.827	0.831	0.804
G3	398	316	67	82	0.825	0.794	0.886	0.809
G4	398	323	71	75	0.820	0.812	0.848	0.816
G5	398	323	62	75	0.839	0.812	0.867	0.825
G6	398	326	46	72	0.876	0.819	0.879	0.847
G7	398	328	55	70	0.856	0.824	0.878	0.840
G8	398	329	41	69	0.889	0.827	0.871	0.857