论文标题
通过动态重量平均和上下文地面真相抽样解决基于激光雷达的对象检测器的类不平衡
Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling
论文作者
论文摘要
自主驾驶系统需要一个3D对象检测器，必须可靠地感知所有当前的道路代理以安全地驾驶环境。但是，现实世界中的驾驶数据集经常遭受数据不平衡问题的困扰，这会导致训练在所有课程中运作良好的模型的困难，从而导致不需要的不平衡的次优性能。在这项工作中，我们提出了一种解决此数据不平衡问题的方法。我们的方法由两个主要组成部分组成：（i）基于激光雷达的3D对象检测器，其每个级别的多个检测头，每个头部的损失都通过动态重量平均值来修改以保持平衡。 （ii）上下文基础真理（GT）采样，我们通过利用语义信息来通过采样地面真相GT对象来增强点云来改进常规的GT采样技术。我们对KITTI和NUSCENES数据集的实验证实了我们提出的方法在处理数据不平衡问题方面的有效性，与现有方法相比，我们提出了更好的检测准确性。
An autonomous driving system requires a 3D object detector, which must perceive all present road agents reliably to navigate an environment safely. However, real-world driving datasets often suffer from the problem of data imbalance, which causes difficulties in training a model that works well across all classes, resulting in an undesired imbalanced sub-optimal performance. In this work, we propose a method to address this data imbalance problem. Our method consists of two main components: (i) a LiDAR-based 3D object detector with per-class multiple detection heads where losses from each head are modified by dynamic weight average to be balanced. (ii) Contextual ground truth (GT) sampling, where we improve conventional GT sampling techniques by leveraging semantic information to augment point cloud with sampled ground truth GT objects. Our experiment with KITTI and nuScenes datasets confirms our proposed method's effectiveness in dealing with the data imbalance problem, producing better detection accuracy compared to existing approaches.
