Semantic segmentation method and system for automatic driving, electronic equipment and medium

A technology of semantic segmentation and automatic driving, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problem of low performance of 3D point cloud semantic segmentation, and achieve the improvement of 3D semantic segmentation effect, efficient understanding, and reduction of computational complexity. degree of effect

Pending Publication Date: 2022-02-08
SOUTHWEST UNIVERSITY
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Problems solved by technology

[0004] The present invention provides a semantic segmentation method, system, electronic equipment and medium for

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  • Semantic segmentation method and system for automatic driving, electronic equipment and medium
  • Semantic segmentation method and system for automatic driving, electronic equipment and medium
  • Semantic segmentation method and system for automatic driving, electronic equipment and medium

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Example Embodiment

[0083] Example 1

[0084] (1) Selection of data sets

[0085] In order to verify the performance of the semantic split network model of the present embodiment, the three-dimensional point cloud data of the present embodiment is derived from a large-scale Semantickitti data set and a small-scale SemanticPoss dataset. The SemantickItti dataset is built in a manner that provides a dense point semantic labeling for a 360-degree scanned Kitti Odometry Benchmark. This data set contains 21 sequences of 43,000 scan data. The 21000 scan data of the No. 00 and 10 sequence is used for training, and the 08 sequence is used to verify, and the 11 to 21 sequence is used to test. SemanticPoss data is a small-scale dataset created by Peking University, which contains 2988 complex scenes with high quality dynamic goals. It follows the same data format specification as Semantickitti. This data set has 6 parts, where 2nd and third parts are used for testing, and the remainder is used for training.

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Abstract

The invention is suitable for the technical field of deep learning and automatic driving, and provides a semantic segmentation method and system for automatic driving, electronic equipment and a medium, and the method comprises the steps of obtaining three-dimensional point cloud data, mapping the three-dimensional point cloud data into a two-dimensional depth map comprising a plurality of pieces of channel data, and forming a sample data set according to the plurality of pieces of channel data; building a semantic segmentation network initial model comprising a first model and a second model, using the sample data set to train the semantic segmentation network initial model, obtaining a target model, where the second model comprises an encoder and a decoder; obtaining target three-dimensional point cloud data, mapping the target three-dimensional point cloud data into a target two-dimensional depth map, inputting the target two-dimensional depth map into the target model, and obtaining a target semantic segmentation result. Through adoption of the method, the problem of low semantic segmentation performance of the three-dimensional point cloud is solved.

Description

technical field [0001] The present invention relates to the technical fields of deep learning and automatic driving, and in particular to a semantic segmentation method, system, electronic equipment and medium for automatic driving. Background technique [0002] With the development of technology, artificial intelligence technology will be applied in more fields and play an increasingly important value. Semantic segmentation is one of the important applications in the field of artificial intelligence, and it is widely used in autonomous driving, video understanding, face recognition systems, intelligent hardware, etc. In the field of autonomous driving, accurate, robust, reliable, and real-time perception and understanding of the traffic environment can be achieved through accurate semantic segmentation of the traffic environment. [0003] At present, most autonomous driving systems use multiple types of sensors with complementary characteristics, such as cameras and radars...

Claims

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Application Information

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IPC IPC(8): G06V20/56G06V10/26G06V10/40G06V10/774G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/253G06F18/214
Inventor 韩先锋程辉先肖国强
Owner SOUTHWEST UNIVERSITY
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