Joint Semantic-Instance Segmentation Method for Intelligent Transportation System
Joint Semantic-Instance Segmentation Method for Intelligent Transportation System
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DOI：
10.1109/tits.2022.3190369
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发表时间：
2023-12
影响因子：
8.5
通讯作者：
Yujie Li;Jin-Lin Cai;Quan Zhou;Huimin Lu
中科院分区：
文献类型：
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作者：
Yujie Li;Jin-Lin Cai;Quan Zhou;Huimin Lu
从传感器获取点云数据，正确理解场景是智能交通系统的核心。点云分割可以帮助智能交通系统区分场景中不同的物体。有些方法通过特征提取网络对点云进行处理，完成分割任务。然而，这些方法对特征提取网络的要求较高，特征的精细程度将直接影响到最终的分割结果。本文提出了一种新的特征提取网络，通过加入编码器-解码器结构，可以从特征映射中提取多尺度局部特征信息。我们认为合并后的多尺度特征得到了更好的特征矩阵，提高了分割任务的性能。我们报告了在S3DIS数据集上的结果，新的特征提取网络极大地改善了语义分割和实例分割任务。
Getting the point cloud data from sensors and correctly understanding the scene is the core of the intelligent transportation system. Point cloud segmentation can help intelligent transportation systems distinguish different objects in the scene. Some methods process the point cloud through a feature extraction network and complete the segmentation task. However, these methods have high requirements on the feature extraction network, and the fineness of the features will directly affect the final segmentation result. In this paper, we propose a new feature extraction network for segmentation by adding an encoder-decoder structure, which can extract the multiscale local feature information from the feature map. In our opinion, the merged multiscale features obtain a better feature matrix, which improves the performance of the segmentation tasks. We report results on the S3DIS dataset, new feature extraction network greatly improves both semantic segmentation and instance segmentation tasks.