论文标题
实时和强大的3D对象检测在路边激光雷达内使用域适应
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation
论文作者
论文摘要
这项工作旨在解决使用基础架构激光雷达的3D对象检测域适应的挑战。我们设计了一个模型DASE毛细管，可以实时检测基于基础设施的LIDARS中的车辆。我们的模型将Pointpillars用作基线模型，并具有其他模块来提高3D检测性能。为了证明我们在DASE毛细管中提出的模块的有效性，我们在两个数据集上训练和评估该模型，开源A9-DATASET和一个在Regensburg下一个项目中创建的半合成基础架构数据集。我们为DASE毛细管检测器中的每个模块做了几组实验，这些实验表明我们的模型在实际A9测试集和半合成A9测试集上的表现优于Se-Propillars基线，同时保持推理速度为45 Hz（22 ms）。我们通过应用转移学习并实现3D map@0 [email protected]，在目标测试集的汽车类别上实现3D [email protected]，使用40个召回位置，将域中从半合成A9-DATASET应用于下一个项目的半合成数据集。
This work aims to address the challenges in domain adaptation of 3D object detection using infrastructure LiDARs. We design a model DASE-ProPillars that can detect vehicles in infrastructure-based LiDARs in real-time. Our model uses PointPillars as the baseline model with additional modules to improve the 3D detection performance. To prove the effectiveness of our proposed modules in DASE-ProPillars, we train and evaluate the model on two datasets, the open source A9-Dataset and a semi-synthetic infrastructure dataset created within the Regensburg Next project. We do several sets of experiments for each module in the DASE-ProPillars detector that show that our model outperforms the SE-ProPillars baseline on the real A9 test set and a semi-synthetic A9 test set, while maintaining an inference speed of 45 Hz (22 ms). We apply domain adaptation from the semi-synthetic A9-Dataset to the semi-synthetic dataset from the Regensburg Next project by applying transfer learning and achieve a 3D [email protected] of 93.49% on the Car class of the target test set using 40 recall positions.
