Joint Semantic-Instance Segmentation Method for Intelligent Transportation System

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 12; pp. 15540 - 15547
• Main Authors: Li, Yujie, Cai, Jintong, Zhou, Quan, Lu, Huimin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Decoding; Encoders-Decoders; Feature extraction; Feature maps; Fineness; Image segmentation; Instance segmentation; Intelligent transportation systems; multiscale fusion; Point cloud compression; Semantic segmentation; Semantics; Task analysis; Three-dimensional displays; Transportation; Transportation networks
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3190369

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.