Smart monitoring using three-dimensional (3D) image sensors has been attracting attention in the context of smart cities. In smart monitoring, object detection from point cloud data acquired by 3D image sensors is implemented for detecting moving objects such as vehicles and pedestrians to ensure safety on the road. However, the features of point cloud data are diversified due to the characteristics of light detection and ranging (LIDAR) units used as 3D image sensors or the install position of the 3D image sensors. Although a variety of deep learning (DL) models for object detection from point cloud data have been studied to date, no research has considered how to use multiple DL models in accordance with the features of the point cloud data. In this work, we propose a feature-based model selection framework that creates various DL models by using multiple DL methods and by utilizing training data with pseudo incompleteness generated by two artificial techniques: sampling and noise adding. It selects the most suitable DL model for the object detection task in accordance with the features of the point cloud data acquired in the real environment. To demonstrate the effectiveness of the proposed framework, we compare the performance of multiple DL models using benchmark datasets created from the KITTI dataset and present example results of object detection obtained through a real outdoor experiment. Depending on the situation, the detection accuracy varies up to 32% between DL models, which confirms the importance of selecting an appropriate DL model according to the situation.
翻译:使用三维(3D)图像传感器的智能监测在智能城市中引起注意。在智能监测中,3D图像传感器从点云中获取的点云数据探测物体,用于探测车辆和行人等移动物体以确保公路安全,但是,由于作为三维图像传感器或3D图像传感器安装位置的光探测和测距(LIDAR)单位的特点,点云数据的特征多样化,点云数据数据利用三维图像传感器利用三维图像传感器的光探测和测距(LIDAR)单元或3D图像传感器的安装位置,因此点云传感器的智能监测吸引了人们注意。虽然迄今为止对从点云数据中获取的点探测物体的多种深度学习(DL)模型进行了研究,但是没有研究根据点云数据数据的特性如何使用多个DL模型进行多重DL模型的探测。在这项工作中,我们提出了一个基于基于多种DL的模型的模型的特性模型选择框架,从而创建了各种DL模型,从而通过两个人工技术(取样和噪音添加)生成的模型,从而确定实际检测结果的DI的模型。 它选择适合的D级数据状况。