3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X Orchestration

The use of edge computing for 3D perception has garnered interest in intelligent transportation systems (ITS) due to its potential to enhance Vehicle-to-Everything (V2X) orchestration through real-time traffic monitoring. The ability to accurately measure depth information in the environment using L...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2023-12, Vol.72 (12), p.1-12
Main Authors: Zhang, Haolin, Mekala, M S, Yang, Dongfang, Isaacs, John, Nain, Zulkar, Park, Ju H., Jung, Ho-Youl
Format: Article
Language:English
Subjects:
3D harmonic loss
Datasets
Detectors
Edge computing
Harmonic analysis
Image edge detection
Intelligent transportation systems
Object detection
Object recognition
Real time
Solid modeling
Space perception
Task analysis
Three-dimensional displays
Vehicle technology
Vehicle-to-Everything (V2X) orchestration
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Summary:The use of edge computing for 3D perception has garnered interest in intelligent transportation systems (ITS) due to its potential to enhance Vehicle-to-Everything (V2X) orchestration through real-time traffic monitoring. The ability to accurately measure depth information in the environment using LiDAR has led to a growing emphasis on 3D detection based on this technology, which has significantly advanced the field of 3D perception. However, the computationally-intensive nature of these operations has made it challenging to meet the real-time deployment requirements using existing methods. The object detection task in the pointcloud domain is hindered by a substantial inconsistency problem caused by its high sparsity, which remains unaddressed. This paper conducts an in-depth analysis of the issue, which has been brought to light by recent research on detecting inconsistency problems in image specialization. To address this problem, we propose a solution in the form of a 3D harmonic loss function, which aims to alleviate the inconsistent predictions based on pointcloud data. In addition, we showcase the viability of optimizing 3D harmonic loss mathematically. Our simulations employ the KITTI dataset and DAIR-V2X-I dataset, and our proposed approach significantly surpasses the performance of benchmark models. Additionally, we validate the efficiency of our proposed model through its deployment on an edge device (Jetson Xavier TX) in a simulated environment.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2023.3291650