论文标题
3D对象检测神经网络系统的安全性强化
Safety-Aware Hardening of 3D Object Detection Neural Network Systems
论文作者
论文摘要
我们研究如何使用单级管道进行3D对象检测的最先进的神经网络可以使安全意识。我们从安全规范（反映其他组件的能力）开始，该规范按临界空间划分了3D输入空间，在临界区域采用了在扰动下的鲁棒性，边界框的质量以及对训练集中证明的虚假负面影响的耐受性的单独标准。在体系结构设计中，我们考虑符号错误传播以允许特征级扰动。随后，我们引入了反映（1）安全规范的专门损失函数，（2）使用单阶段检测体系结构，最后，（3）（3）在扰动下的鲁棒性表征。我们还替换了通过安全性无限含量包含算法的常见的非最大抑制后处理算法，以维持神经网络所产生的安全性要求。通过扩展最先进的像素检测器来详细介绍该概念，该探测器可以在鸟类的眼视图中使用点云输入来创建对象边界框。
We study how state-of-the-art neural networks for 3D object detection using a single-stage pipeline can be made safety aware. We start with the safety specification (reflecting the capability of other components) that partitions the 3D input space by criticality, where the critical area employs a separate criterion on robustness under perturbation, quality of bounding boxes, and the tolerance over false negatives demonstrated on the training set. In the architecture design, we consider symbolic error propagation to allow feature-level perturbation. Subsequently, we introduce a specialized loss function reflecting (1) the safety specification, (2) the use of single-stage detection architecture, and finally, (3) the characterization of robustness under perturbation. We also replace the commonly seen non-max-suppression post-processing algorithm by a safety-aware non-max-inclusion algorithm, in order to maintain the safety claim created by the neural network. The concept is detailed by extending the state-of-the-art PIXOR detector which creates object bounding boxes in bird's eye view with inputs from point clouds.
