To ensure safe urban driving for autonomous platforms, it is crucial not only
to develop high-performance object detection techniques but also to establish a
diverse and representative dataset that captures various urban environments and
object characteristics. To address these two issues, we have constructed a
multi-class 3D LiDAR dataset reflecting diverse urban environments and object
characteristics, and developed a robust 3D semi-supervised object detection
(SSOD) based on a multiple teachers framework. This SSOD framework categorizes
similar classes and assigns specialized teachers to each category. Through
collaborative supervision among these category-specialized teachers, the
student network becomes increasingly proficient, leading to a highly effective
object detector. We propose a simple yet effective augmentation technique,
Pie-based Point Compensating Augmentation (PieAug), to enable the teacher
network to generate high-quality pseudo-labels. Extensive experiments on the
WOD, KITTI, and our datasets validate the effectiveness of our proposed method
and the quality of our dataset. Experimental results demonstrate that our
approach consistently outperforms existing state-of-the-art 3D semi-supervised
object detection methods across all datasets. We plan to release our
multi-class LiDAR dataset and the source code available on our Github
repository in the near future.

为了确保自主驾驶平台的安全行驶，除了开发高性能的物体检测技术外，还需要建立一个反映各种城市环境和物体特征的多样化和代表性的数据集。为了解决这两个问题，我们构建了一个多类别的 3D LiDAR 数据集，反映了多样化的城市环境和物体特征，并开发了基于多个教师框架的强大的 3D 半监督目标检测器。我们提出了一种简单而有效的增强技术，基于饼状点补偿（PieAug）的增强，以使教师网络生成高质量的伪标签。对 WOD、KITTI 和我们的数据集进行的大量实验验证了我们提出的方法的有效性和数据集的质量。实验结果表明，我们的方法始终优于现有的最先进的 3D 半监督目标检测方法。我们计划在不久的将来在我们的 Github 存储库上发布我们的多类别 LiDAR 数据集和源代码。

合作监督的力量：增强的三维半监督目标检测的多教师框架

Power of Cooperative Supervision: Multiple Teachers Framework for  Enhanced 3D Semi-Supervised Object Detection

Although robot-based automation in chemistry laboratories can accelerate the
material development process, surveillance-free environments may lead to
dangerous accidents primarily due to machine control errors. Object detection
techniques can play vital roles in addressing these safety issues; however,
state-of-the-art detectors, including single-shot detector (SSD) models, suffer
from insufficient accuracy in environments involving complex and noisy scenes.
With the aim of improving safety in a surveillance-free laboratory, we report a
novel deep learning (DL)-based object detector, namely, DenseSSD. For the
foremost and frequent problem of detecting vial positions, DenseSSD achieved a
mean average precision (mAP) over 95% based on a complex dataset involving both
empty and solution-filled vials, greatly exceeding those of conventional
detectors; such high precision is critical to minimizing failure-induced
accidents. Additionally, DenseSSD was observed to be highly insensitive to the
environmental changes, maintaining its high precision under the variations of
solution colors or testing view angles. The robustness of DenseSSD would allow
the utilized equipment settings to be more flexible. This work demonstrates
that DenseSSD is useful for enhancing safety in an automated material synthesis
environment, and it can be extended to various applications where high
detection accuracy and speed are both needed.

本文介绍了一种新的基于深度学习的物体检测器 DenseSSD，能够有效地提高化学实验室自动化工作的安全性和效率。该物体检测器可以在复杂和嘈杂的场景中实现超过 95% 的平均精度，并且对于环境变化方面相当鲁棒。这种方法的可扩展性很强，在其他需要高精度和高速检测的应用中都有很好的应用前景。