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Remote Sensing
Volume 15
Issue 3
10.3390/rs15030627
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Article

Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System

by
Pei An
1,
Junxiong Liang
2,
Xing Hong
2,
Siwen Quan
3,
Tao Ma
4,
Yanfei Chen
1,
Liheng Wang
1 and
Jie Ma
2,*
1
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
2
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430072, China
3
School of Electronic and Control Engineering, Changan University, Xi’an 710064, China
4
Institute of Computer Application, China Academy of Engineer Physics, Mianyang 621900, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(3), 627; https://doi.org/10.3390/rs15030627
Submission received: 8 December 2022 / Revised: 9 January 2023 / Accepted: 18 January 2023 / Published: 20 January 2023
(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)
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Abstract

Three dimensional (3D) object detection with an optical camera and light detection and ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The current 3D object detection method is based on deep learning and is data-hungry. Recently, semi-supervised 3D object detection (SSOD-3D) has emerged as a technique to alleviate the shortage of labeled samples. However, it is still a challenging problem for SSOD-3D to learn 3D object detection from noisy pseudo labels. In this paper, to dynamically filter the unreliable pseudo labels, we first introduce a self-paced SSOD-3D method SPSL-3D. It exploits self-paced learning to automatically adjust the reliability weight of the pseudo label based on its 3D object detection loss. To evaluate the reliability of the pseudo label in accuracy, we present prior knowledge based SPSL-3D (named as PSPSL-3D) to enhance the SPSL-3D with the semantic and structure information provided by a LiDAR-camera system. Extensive experimental results in the public KITTI dataset demonstrate the efficiency of the proposed SPSL-3D and PSPSL-3D.
Keywords: 3D object detection; semi-supervised learning; self-paced learning; LiDAR-camera system 3D object detection; semi-supervised learning; self-paced learning; LiDAR-camera system

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MDPI and ACS Style

An, P.; Liang, J.; Hong, X.; Quan, S.; Ma, T.; Chen, Y.; Wang, L.; Ma, J. Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System. Remote Sens. 2023, 15, 627. https://doi.org/10.3390/rs15030627

AMA Style

An P, Liang J, Hong X, Quan S, Ma T, Chen Y, Wang L, Ma J. Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System. Remote Sensing. 2023; 15(3):627. https://doi.org/10.3390/rs15030627

Chicago/Turabian Style

An, Pei, Junxiong Liang, Xing Hong, Siwen Quan, Tao Ma, Yanfei Chen, Liheng Wang, and Jie Ma. 2023. "Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System" Remote Sensing 15, no. 3: 627. https://doi.org/10.3390/rs15030627

APA Style

An, P., Liang, J., Hong, X., Quan, S., Ma, T., Chen, Y., Wang, L., & Ma, J. (2023). Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System. Remote Sensing, 15(3), 627. https://doi.org/10.3390/rs15030627

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