Lidar 3D Target Detection Based on Improved PointPillars

Table 1. Hyperparameter configuration of Swin-T module
View table
Table 1. Hyperparameter configuration of Swin-T module
Model Depth Head
M1 （1，3，1）（2，4，8）
M2 （2，6，2）（2，4，8）
M3 （2，2，6）（2，4，8）
M4 （2，6，2）（2，4，2）
M5 （4，8，4）（4，8，4）

Table 2. Comparison of accuracy rates of different Swin-T hyperparameter configurations
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Table 2. Comparison of accuracy rates of different Swin-T hyperparameter configurations
Model Easy Moderate Hard Average
M1 50.17 40.42 38.7 43.09
M2 94.12 89.55 88.48 90.71
M3 94.23 89.77 88.8 90.93
M4 94.13 89.23 88.15 90.5
M5 94.57 89.65 88.75 90.99

Table 3. Comparison of test results

View table

Table 3. Comparison of test results

Model	Easy	Moderate	Hard	Average
PointPillars^［6］	90.77	89.61	88.47	89.61
SECOND^［5］	90.76	89.77	88.82	89.78
SECOND-IoU^［5］	89.72	88.73	88.33	88.92
PointRCNN^［21］	90.76	89.58	89.03	89.79
PointRCNN-IoU^［21］	90.70	89.32	88.87	89.63
Part-A2-Free^［22］	90.68	89.00	88.64	89.44
AS-PointPillars^［12］	90.48	88.32	86.51	88.44
AP-PointPillars^［12］	90.68	88.92	86.90	88.83
Proposed model	94.23	89.77	88.8	90.93

Table 4. Comparison of GPU memory usage and running speed before and after algorithm improvement
View table
Table 4. Comparison of GPU memory usage and running speed before and after algorithm improvement
Model GPU memory /MB Running speed /s
PointPillars 1267 0.036
Proposed model 1359 0.058

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Dejiang Chen, Wenjun Yu, Yongbin Gao. Lidar 3D Target Detection Based on Improved PointPillars[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1028012

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Paper Information

Category: Remote Sensing and Sensors

Received: Mar. 1, 2022

Accepted: May. 7, 2022

Published Online: May. 23, 2023

The Author Email: Wenjun Yu (yuwenjun@sues.edu.cn)

DOI:10.3788/LOP220840

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Hyperparameter configuration of Swin-T module

Table 1. Hyperparameter configuration of Swin-T module

Table 2. Comparison of accuracy rates of different Swin-T hyperparameter configurations

Table 2. Comparison of accuracy rates of different Swin-T hyperparameter configurations

Table 3. Comparison of test results

Table 3. Comparison of test results

Table 4. Comparison of GPU memory usage and running speed before and after algorithm improvement

Table 4. Comparison of GPU memory usage and running speed before and after algorithm improvement