融合密集连接和高斯距离的三维目标检测算法

doi:10.13229/j.cnki.jdxbgxb.20230105

Abstract

Abstract:

To enhance the perception of small objects， based on the F-PointNet network，the FDG-PointNet 3D object detection model is proposed by combining dense connection and Gaussian distance features. Gaussian distance features is fused as additional attention features， and it effectively solves the low accuracy of instance segmentation in the F-PointNet network and enhances the noise filtering in the point cloud view cone. Based on the characteristics that dense connection can enhance feature extraction， the dense connection is used to improve PointNet++ network and enhance point cloud feature reuse. It alleviates low degree of feature extraction and gradient disappearance for small objects in the feature extraction process， and improves the accuracy of 3D object bounding box regression. The experimental results show that the proposed algorithm outperforms the benchmark method F-PointNet in three levels （easy， moderate， and hard） for the detection of car， pedestrian， and cyclist， which can achieve the average detection accuracy of 71.12%， 61.23%， and 55.71% for car， pedestrian， and cyclist at moderate level. It has the most significant improvement for pedestrian detection， and can increase 5.5% and 3.1% at easy and moderate levels， respectively. In summary，compared to F-PointNet algorithm， the proposed FDG-PointNet algorithm effectively solves the low accuracy of small objects detection and has strong applicability.

Key words: computer science and technology, 3D object detection, lidar, dense connectivity, gaussian distance

CLC Number:

TP391.4

Xin CHENG,Sheng-xian LIU,Jing-mei ZHOU,Zhou ZHOU,Xiang-mo ZHAO. 3D object detection algorithm fusing dense connectivity and Gaussian distance[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3589-3600.

Figures/Tables 13

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References 28

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方法	输入	总体精度OA/%
Subvolume	voxels	89.2
PointNet^［11］	point cloud	89.2
Kd-net^［26］	point cloud	90.6
SO-net^［27］	point cloud	90.9
PointNet++^［12］	point cloud	90.7
本文	point cloud	91.3

方法	mean	aero	bag	cap	car	chair	ear phone	guitar	knife	lamp	laptop	motor	mug	pistol	rocket	skate board	table
PointNe^t［11］	83.7	83.4	78.7	82.5	74.9	89.6	73.0	91.5	85.9	80.8	95.3	65.2	93.0	81.2	57.9	72.8	80.6
SSCN^［28］	84.7	81.6	81.7	81.9	75.2	90.2	74.9	93.0	86.1	84.7	95.6	66.7	92.7	81.6	60.6	82.9	82.1
PointNet++^［12］	85.1	82.4	79.0	87.7	77.3	90.8	71.8	91.0	85.9	83.7	95.3	71.6	94.1	81.3	58.7	76.4	82.6
本文	85.3	82.6	82.6	86.3	78.1	91.0	71.1	90.8	87.6	83.1	96.0	72.0	94.4	81.0	58.2	76.8	81.6

模型	汽车			行人			骑行人
模型	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
F-PointNet（v1）	87.82	82.44	74.50	70.38	62.07	56.77	76.72	57.71	52.88
本文	87.90	82.24	74.53	71.61	65.07	57.39	78.55	58.45	54.95

模型	汽车			行人			骑行人
模型	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
F-PointNet（v1）	83.26	69.28	62.56	66.04	57.05	49.84	70.52	51.50	50.95
本文	83.83	69.73	63.10	67.08	57.90	50.90	71.13	52.59	49.40

密集连接	高斯距离	汽车			行人			骑行人
密集连接	高斯距离	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
—	—	83.76	70.92	63.65	64.75	58.11	51.23	73.15	54.89	51.62
√	—	81.18	71.34	63.82	68.15	59.84	52.47	75.03	55.99	52.74
—	√	83.81	70.64	63.77	66.99	59.10	52.25	76.85	55.81	51.67
√	√	84.39	71.12	64.28	70.21	61.23	53.27	75.37	55.71	51.71

3D object detection algorithm fusing dense connectivity and Gaussian distance

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模型	汽车			行人			骑行人
模型	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
MV3D（BV+FV）^［18］	71.19	56.60	55.30	N/A	N/A	N/A	N/A	N/A	N/A
MV3D（BV+FV+RGB）^［18］	71.29	62.68	56.56	N/A	N/A	N/A	N/A	N/A	N/A
RT3D^［29］	72.85	61.64	64.38	N/A	N/A	N/A	N/A	N/A	N/A
PointFusion^［22］	77.92	63.00	53.27	33.36	28.04	23.38	49.34	29.42	26.98
VoxelNet^［5］	81.97	65.46	62.85	57.86	53.42	48.87	67.17	47.65	45.11
F-PointNet（v1）^［20］	83.26	69.28	62.56	66.04	57.05	49.84	70.52	51.50	50.95
F-PointNet（v2）^［20］	83.76	70.92	63.65	64.75	58.11	51.23	73.15	54.89	51.62
本文	84.39	71.12	64.28	70.21	61.23	53.27	75.37	55.71	51.71