提升视觉3D目标检测中背景相似物体的识别能力
ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object Detection
- 引入2D区域定向注意力机制，聚焦物体存在区域
- 在nuScenes上相较BEVDepth提升检测性能
- 适合关注自动驾驶中弱特征物体检测的研究者
基于视觉的鸟瞰图（BEV）3D目标检测在自动驾驶中日益流行。然而，从摄像头视角看与背景高度相似的物体仍难以被现有方法有效检测。本文提出一种基于BEV的3D目标检测网络ROA-BEV，通过2D区域定向注意力机制，使主干网络更专注于物体所在区域的特征学习。此外，该方法通过多尺度结构进一步增强特征表达能力，每个ROA模块采用大卷积核以确保感受野足够覆盖大尺寸物体。在nuScenes数据集上的实验表明，ROA-BEV在BEVDepth基础上实现了性能提升。代码将开源至https://github.com/DFLyan/ROA-BEV。
原文摘要 · Abstract (English)
Vision-based Bird's-Eye-View (BEV) 3D object detection has recently become popular in autonomous driving. However, objects with a high similarity to the background from a camera perspective cannot be detected well by existing methods. In this paper, we propose a BEV-based 3D Object Detection Network with 2D Region-Oriented Attention (ROA-BEV), which enables the backbone to focus more on feature learning of the regions where objects exist. Moreover, our method further enhances the information feature learning ability of ROA through multi-scale structures. Each block of ROA utilizes a large kernel to ensure that the receptive field is large enough to catch information about large objects. Experiments on nuScenes show that ROA-BEV improves the performance based on BEVDepth. The source codes of this work will be available at https://github.com/DFLyan/ROA-BEV.
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