基于特征增稳的混合固态激光雷达目标检测

doi:10.19562/j.chinasae.qcgc.2024.06.008

Abstract

Abstract:

Stable point cloud feature extraction is crucial for 3D object detection using LiDAR. For the limitation of existing deep learning algorithms that can only handle point clouds from mechanically rotating LiDAR but lack support for semi solid-state LiDAR data， in this paper a target detection model suitable for semi solid-state LiDAR based on IA-SSD is established. Firstly， Cloth Simulation Filtering （CSF） is added at the front end of point cloud encoding for ground suboptimal filtering. Secondly， an attention mechanism composed of local feature fusion and global bilinear regularization layers is utilized to promote the fusion of geometric information and feature information of point clouds from both local and global perspectives. Thirdly， GhostGConv is employed to replace the original inefficient point-by-point convolution， and enhanced feature interaction of point clouds is achieved through channel shuffling mechanism to construct an enhanced feature extraction network. Finally， the above modules are integrated into the point detector IA-SSD to complete model construction. The validation results conducted on the SimoSet semi solid-state lidar dataset show that the proposed method significantly outperforms other algorithms supported by the SimoSet dataset in terms of accuracy metrics. In the medium difficulty detection tasks on the KITTI dataset， the proposed method enhances the average detection accuracy of IA-SSD in the three categories by 1.17， 1.47， and 0.5 percentage points， respectively.

Key words: vehicle engineering, environmental perception, object detection, semi solid-state LiDAR, feature stability enhancement

Lisheng Jin,Hongyu Zhang,Baicang Guo. Semi Solid-State LiDAR Object Detection Algorithm Enhanced by Feature Stability Enhancement[J].Automotive Engineering, 2024, 46(6): 1015-1024.

Figures/Tables 10

References 27

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算法	范式	类别	AP_3D/%
算法	范式	类别	LEVEL_1	LEVEL_2
SECOND	体素	Car	80.23	65.71
		Cyc	81.52	45.08
		Ped	78.16	38.92
PointPillars	体素	Car	78.73	63.15
		Cyc	84.05	47.13
		Ped	81.50	42.55
SA-SSD	体素	Car	82.79	68.93
		Cyc	86.36	47.87
		Ped	85.28	45.29
Point-RCNN	点	Car	80.65	67.00
		Cyc	85.27	47.53
		Ped	85.45	21.87
Our	点	Car	84.53	70.65
		Cyc	86.68	48.23
		Ped	85.85	45.24

算法	年份	3D Car （IoU=0.7）			3D Ped（IoU=0.5）			3D Cyc （IoU=0.5）
算法	年份	Easy	Mod	Hard	Easy	Mod	Hard	Easy	Mod	Hard
VoxelNet^［4］	2018	77.47	65.11	57.73	39.48	33.69	31.5	61.22	48.36	44.37
SECOND^［5］	2018	84.65	75.96	68.71	45.31	35.52	33.14	75.83	60.82	53.67
PointPillars^［6］	2019	82.58	74.31	68.99	51.45	41.92	38.89	77.10	58.65	51.92
Point-GNN^［25］	2020	88.33	79.47	72.29	51.92	43.77	40.10	78.60	63.48	57.08
PointRCNN^［24］	2020	86.96	75.64	70.70	51.92	43.77	40.10	78.60	63.48	57.08
TANet^［26］	2020	84.39	75.94	68.82	53.72	44.34	40.49	75.70	59.44	52.53
Part-A^2［27］	2020	87.81	78.49	73.51	53.10	43.35	40.06	79.17	63.52	56.93
IA-SSD^［17］	2022	88.87	80.32	75.10	49.01	41.20	38.03	80.78	66.01	58.12
Ours		89.82	81.49	75.78	50.88	42.67	39.82	81.21	66.51	58.43

算法	AP_3D/% （LEVEL_1）
算法	Car	Cyc	Ped
IA-SSD	82.41	85.46	85.28
+CSF	83.32	85.94	85.51
+LCF&GBR	84.50	86.67	85.84
+GhostGConv	84.41	86.56	85.75
+通道混洗	84.53	86.68	85.85

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Semi Solid-State LiDAR Object Detection Algorithm Enhanced by Feature Stability Enhancement

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