RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving

3D Object detection is a critical mission of the perception system of a self-driving vehicle. Existing bounding box-based methods are hard to train due to the need to remove duplicated detections in the post-processing stage. In this paper, we propose a center point-based deep neural network (DNN) a...

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Bibliographic Details
Published in:Applied sciences 2021-06, Vol.11 (12), p.5621
Main Authors: An, Kang, Chen, Yixin, Wang, Suhong, Xiao, Zhifeng
Format: Article
Language:English
Subjects:
Ablation
absolute pose
Accuracy
Algorithms
Autonomous cars
autonomous driving
Backbone
Boxes
Cameras
CenterNet
Datasets
Deep learning
Depth perception
feature fusion
feature pyramid network
Neural networks
object detection
Post-production processing
Semantics
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Summary:3D Object detection is a critical mission of the perception system of a self-driving vehicle. Existing bounding box-based methods are hard to train due to the need to remove duplicated detections in the post-processing stage. In this paper, we propose a center point-based deep neural network (DNN) architecture named RCBi-CenterNet that predicts the absolute pose for each detected object in the 3D world space. RCBi-CenterNet is composed of a recursive composite network with a dual-backbone feature extractor and a bi-directional feature pyramid network (BiFPN) for cross-scale feature fusion. In the detection head, we predict a confidence heatmap that is used to determine the position of detected objects. The other pose information, including depth and orientation, is regressed. We conducted extensive experiments on the Peking University/Baidu-Autonomous Driving dataset, which contains more than 60,000 labeled 3D vehicle instances from 5277 real-world images, and each vehicle object is annotated with the absolute pose described by the six degrees of freedom (6DOF). We validated the design choices of various data augmentation methods and the backbone options. Through an ablation study and an overall comparison with the state-of-the-art (SOTA), namely CenterNet, we showed that the proposed RCBi-CenterNet presents performance gains of 2.16%, 2.76%, and 5.24% in Top 1, Top 3, and Top 10 mean average precision (mAP). The model and the result could serve as a credible benchmark for future research in center point-based object detection.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11125621