Integrated graph convolution and multi-scale features for the overhead catenary system point cloud semantic segmentation

doi:10.11947/j.AGCS.2024.20230198

Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (8): 1624-1633.doi: 10.11947/j.AGCS.2024.20230198

• Photogrammetry and Remote Sensing • Previous Articles Next Articles

Integrated graph convolution and multi-scale features for the overhead catenary system point cloud semantic segmentation

Tao XU¹(), Yuanwei YANG¹(), Xianjun GAO¹^,², Zhiwei WANG³, Yue PAN³, Shaohua LI¹, Lei XU⁴, Yanjun WANG⁵^,⁶, Bo LIU², Jing YU⁷, Fengmin WU⁷, Haoyu SUN¹

^1.School of Geosciences, Yangtze University, Wuhan 430100, China
^2.Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China
^3.Region Surveying and Mapping Geographic Information Center, Hohhot 010050, China
^4.China Railway Design Corporation, Tianjin 300308, China
^5.National-local Joint Engineering Laboratory of Geo-spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
^6.Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
^7.Chongqing Geomatics and Remote Sensing Center, Chongqing 401147, China

Received:2023-06-08 Online:2024-09-25 Published:2024-09-25
Contact: Yuanwei YANG E-mail:2021720578@yangtze.edu.cn;2021720578@yangtze.edu.cn;yyw_08@yangtzeu.edu.cn
About author:XU Tao (1998—), male, postgraduate, majors in the semantic segmentation of 3D point cloud data. E-mail: 2021720578@yangtze.edu.cn
Supported by:
The Open Fund of National Engineering Laboratory for Digital Construction and Evaluation Technology of Urban Rail Transit(2023ZH01);Tianjin Science and Technology Plan Project(23YFYSHZ00190);The Natural Science Foundation of Chongqing Province(CSTB2022NSCQ-MSX1484);Open Fund of Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources(MEMI-2021-2022-08);The Open Fund of Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology(E22205);Hunan Provincial Natural Science Foundation Project Department Union Fund(2024JJ8327);Jiangxi Provincial Natural Science Foundation(20232ACB204032);Yangtze University College Student Innovation Project(Yz2023013)

Abstract

Abstract:

Accurately segmenting the catenary is essential for extracting its components and detecting geometric parameters. In fact, the catenary scene is complex, with significant differences in size between internal components. There are many components with similar and connected semantic information, which makes it difficult for existing deep learning methods to accurately complete catenary point cloud semantic segmentation tasks. To address this issue, this paper proposes a neural network named GDM-Net that leverages graph convolution and multi-level features. GDM-Net includes a graph-based local feature extractor that enhances local feature extraction of the catenary point cloud, a double efficient channel attention module that considers the extraction of global and salient features of the catenary point cloud, and a refinement module of multi-scale feature fusion that improves segmentation accuracy by extracting and fusing multi-scale information of the catenary. The proposed network significantly improves the point cloud segmentation ability of catenary components, particularly at the intersection. Based on qualitative and quantitative analysis of the overhead catenary system dataset, the method is verified to achieve the highest accuracy among five other point cloud deep learning methods. The OA, mIoU, and F₁ accuracy indices reach 96.73%, 91.06%, and 95.28%, respectively. Qualitative comparisons demonstrate that the proposed network effectively reduces the misclassification problem of component links and improves the integrity of catenary component segmentation.

Key words: LiDAR, overhead catenary system, graph convolution, attention mechanism, multi-scale feature fusion, point cloud semantic segmentation

CLC Number:

P237

Tao XU, Yuanwei YANG, Xianjun GAO, Zhiwei WANG, Yue PAN, Shaohua LI, Lei XU, Yanjun WANG, Bo LIU, Jing YU, Fengmin WU, Haoyu SUN. Integrated graph convolution and multi-scale features for the overhead catenary system point cloud semantic segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(8): 1624-1633.

Figures/Tables 15

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数据集	单臂	双臂	训练集		验证集		测试集
数据集	单臂	双臂	单臂	双臂	单臂	双臂	单臂	双臂
场景数	68	20	55	15	11	4	2	1
点的数量	3.54×10⁷	1.4×10⁷	2.9×10⁷	1.1×10⁷	5.7×10⁶	2.8×10⁶	1.04×10⁶	7×10⁵

方法	评价精度	承力索	定位器	斜腕臂	直腕臂	弹性吊索	定位管	背景	吊弦	接触线
PointNet	P	83.20	70.23	77.09	74.70	79.42	62.24	50.04	88.17	98.29
	R	90.54	81.88	92.94	98.16	63.18	87.72	48.32	57.58	97.03
	mIoU	76.55	60.79	72.82	73.67	64.29	61.25	50.09	53.46	95.42
	F₁值	86.72	75.61	84.28	84.84	70.38	72.82	49.17	69.67	97.66
PointNet＋＋	P	86.86	96.36	94.63	94.56	84.65	91.63	87.76	83.66	96.1
	R	90.65	93.65	96.28	92.34	86.33	93.36	87.36	83.09	98.19
	mIoU	76.12	86.95	91.36	91.92	72.58	88.50	79.36	80.91	94.93
	F₁值	88.71	94.99	95.46	93.45	85.48	92.49	87.56	83.37	97.13
DGCNN	P	92.81	98.55	96.62	96.56	86.80	93.07	87.98	85.61	99.33
	R	92.69	94.74	94.89	96.56	87.80	95.51	87.29	85.69	99.19
	mIoU	86.48	93.44	91.85	93.34	77.46	89.17	77.99	74.90	98.53
	F₁值	92.75	96.61	95.75	96.56	87.30	94.27	87.63	85.65	99.26
PointNeXt	P	90.34	98.56	99.27	94.70	95.18	96.55	93.31	94.25	98.87
	R	96.65	96.07	94.98	98.87	85.69	98.69	92.86	88.56	99.71
	mIoU	87.60	94.75	94.32	93.69	82.13	95.33	87.06	84.03	98.58
	F₁值	93.39	97.30	97.08	96.74	90.19	97.61	93.08	91.32	99.29
Pix4point	P	96.49	96.59	99.36	94.76	91.56	87.91	91.12	89.71	99.56
	R	97.26	93.67	95.05	98.48	93.86	97.85	92.13	88.46	99.44
	mIoU	91.13	90.67	94.47	93.39	86.39	86.24	89.90	77.89	99.00
	F₁值	96.87	95.11	97.16	96.59	92.70	92.61	91.62	89.08	99.50
GDM－Net	P	96.25	98.69	98.15	96.24	94.53	91.64	93.61	91.84	99.53
	R	96.85	94.67	95.16	97.81	90.18	97.91	92.94	87.38	99.61
	mIoU	91.45	93.49	93.48	94.21	87.71	89.88	89.06	81.09	99.15
	F₁值	95.53	96.64	96.63	97.02	92.31	94.67	93.27	89.56	99.57

方法	P	R	OA	mIoU	F₁值
PointNet	75.93	79.70	88.26	64.09	77.77
PointNet＋＋	90.69	91.25	92.34	84.37	90.96
DGCNN	93.04	92.71	94.93	87.02	92.87
PointNeXt	95.67	94.68	96.05	90.83	95.17
Pix4point	94.12	95.13	96.54	89.90	94.62
GDM－Net	95.61	94.94	96.73	91.06	95.28

方法	参数	FLOPs	每点测试时间/μs
PointNet	3.55×10⁶	58.53×10⁹	16.21
PointNet＋＋	1.24×10⁶	10.72×10⁹	20.51
DGCNN	2.64×10⁶	64.24×10⁹	17.35
PointNeXt	41.60×10⁶	84.80×10⁹	18.81
Pix4Point	23.70×10⁶	190.00×10⁹	21.36
GDM－Net	3.42×10⁶	133.87×10⁹	24.41

方法	mIoU	类别名称
方法	mIoU	轨道	道床	悬链线	围栏	植被	立柱	钢结构	地面	建筑物	支持装置	背景点
PointNet	11.72	5.51	6.83	13.66	7.01	38.20	2.36	7.66	25.49	6.45	7.56	8.21
PointNet＋＋	34.28	25.86	36.63	56.36	15.63	60.82	9.62	23.36	57.36	32.36	28.69	30.35
DGCNN	24.05	21.08	16.89	26.31	17.21	61.15	9.63	8.65	38.15	19.63	9.56	36.30
PointNeXt	75.19	80.72	73.23	62.92	81.61	88.28	65.39	74.64	83.36	90.82	60.61	65.59
Pix4Point	71.54	71.84	44.66	74.19	45.19	89.99	64.34	67.81	79.72	91.58	62.64	94.94
GDM－Net	81.52	90.63	78.69	76.33	88.37	93.61	68.37	72.63	85.39	85.68	68.36	88.65

Integrated graph convolution and multi-scale features for the overhead catenary system point cloud semantic segmentation

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