Real-time and accurate 3-D object detection is one of the foundational technologies for environmental perception in autonomous vehicles. However, the existing second-stage anchor-based 3-D object detection algorithms have high accuracy, but they are challenging in terms of computation complexity and latency. Due to poor perception of spatial features, the accuracy of the existing single-stage anchor-free detection algorithms with low latency are difficult to be implemented into autonomous vehicles. Therefore, we focus on enhancing the spatial perception ability of the anchor-free detection network based on CenterPoints. In this paper, we propose a single-stage anchor-free 3-D object detector CenterPoint-Space-Enhancement (CenterPoint-SE) algorithm and construct an efficient 3-D backbone network to extract fine-grained spatial geometric features by introducing a spatial attention mechanism and residual structure. At the same time, a powerful spatial semantic feature fusion module, the enhancement of feature fusion (EF-Fusion), is designed. In addition, we add a lightweight IoU prediction branch to improve the algorithm’s perception of various object sizes. Finally, we add a foreground point segmentation auxiliary training branch to enable the 3-D backbone to obtain object boundary features. We use the ONCE dataset to train and validate the proposed model, and the results showed that the proposed CenterPoint-SE achieves 70.33 mAP and an inference speed of 17.15 FPS, outperforming other methods.


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    Title :

    CenterPoint-SE: A Single-Stage Anchor-Free 3-D Object Detection Algorithm With Spatial Awareness Enhancement


    Contributors:
    Wang, Hai (author) / Tao, Le (author) / Cai, Yingfeng (author) / Chen, Long (author) / Li, Yicheng (author) / Sotelo, Miguel Angel (author) / Li, Zhixiong (author)

    Published in:

    Publication date :

    2023-10-01


    Size :

    14666211 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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