用不一致性筛选数据，一半标注量达到随机采样效果
Inconsistency-based Active Learning for LiDAR Object Detection
- 基于检测框数量差异选择难样本
- 仅用50%标注数据达到同等mAP
- 适合数据标注成本高的自动驾驶场景
自动驾驶中的目标检测深度学习模型近期取得显著性能提升，已在全球车辆中部署。然而，当前模型需要越来越大的训练数据集，而数据采集与标注成本高昂，亟需优化训练流程的新策略。主动学习是一种有前景的方法，在图像领域已有广泛研究。本文将该思想扩展至激光雷达（LiDAR）领域，提出多种基于不一致性的样本选择策略，并在不同设置下评估其有效性。结果表明，仅使用基于检测框数量的简单不一致性方法，即可在仅使用50%标注数据的情况下，达到与随机采样相同的mAP性能。
原文摘要 · Abstract (English)
Deep learning models for object detection in autonomous driving have recently achieved impressive performance gains and are already being deployed in vehicles worldwide. However, current models require increasingly large datasets for training. Acquiring and labeling such data is costly, necessitating the development of new strategies to optimize this process. Active learning is a promising approach that has been extensively researched in the image domain. In our work, we extend this concept to the LiDAR domain by developing several inconsistency-based sample selection strategies and evaluate their effectiveness in various settings. Our results show that using a naive inconsistency approach based on the number of detected boxes, we achieve the same mAP as the random sampling strategy with 50% of the labeled data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。