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A traffic scene analysis method based on a multi-task network

An analysis method and traffic scene technology, applied in the field of multi-task network design for real-time traffic scene analysis, can solve problems such as poor real-time performance, achieve high accuracy, good real-time performance, and improve segmentation and detection effects

Inactive Publication Date: 2018-12-11
DALIAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0004] In the actual traffic scene application, if the traffic scene analysis is carried out and the semantic segmentation and object detection are realized at the same time, two networks must be run at the same time, which requires powerful computing performance and poor real-time performance.

Method used

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  • A traffic scene analysis method based on a multi-task network
  • A traffic scene analysis method based on a multi-task network
  • A traffic scene analysis method based on a multi-task network

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Embodiment Construction

[0023] Describe the specific embodiment of the present invention in detail below in conjunction with technical scheme and accompanying drawing, a kind of multi-task network design method for real-time traffic scene analysis comprises the following steps:

[0024] A. Multi-task network structure design

[0025] The multi-task network includes encoder, segmentation decoder and detection decoder. The encoder includes a convolutional layer and a downsampling layer, and the convolutional layer adopts a three-layer residual learning unit in a deep residual network, which is used to extract feature information from an original image to obtain a feature map; the described The convolution kernel size of the downsampling layer is 3×3 and the step size is 2, which is used to reduce the size of the feature map; at the end of the encoder, a spatial pyramid pooling layer is included to extract information of different scales in the feature map . Through a hierarchical combination of convo...

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Abstract

The invention discloses a traffic scene analysis method based on a multi-task network, comprising the following steps: a multi-task network is divided into an encoder, a partition decoder and a detection decoder, wherein the encoder extracts the features of the image and extracts the multi-scale information from the feature map; the segmentation decoder enlarges the size of the feature map and fuses it with the feature map; the detection decoder processes the input characteristic map and outputs a corresponding target detection result; using a deep learning framework Tensorflow to configure, train and test the above multitasking network. The multi-task network of the invention can extract abundant image features, make up for the loss of image detail information caused by downsampling in the encoder, and help to improve the segmentation and detection effect. The invention designs a multi-task network structure, which can realize semantic segmentation and target detection of traffic scene images through one-time back propagation, and has better real-time performance and higher accuracy rate.

Description

technical field [0001] The invention belongs to the field of safety assisted driving, in particular to a multi-task network design method for real-time traffic scene analysis. Background technique [0002] Vision-based traffic scene parsing has important applications in intelligent transportation systems. Semantic segmentation and object detection are two main tasks in traffic scene parsing. Traditional methods to solve these tasks, such as support vector machine (SVM), adaptive boosting algorithm (AdaBoost), random forest iterative method (random forest), etc., have poor generalization and robustness. In recent years, deep learning has made breakthroughs and has been widely used in the field of image processing. The deep learning method automatically extracts rich features from complex data, which makes the model have better generalization ability, and the extracted features can be used for different tasks such as semantic segmentation and object detection. [0003] Sema...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06N3/04
CPCG06V20/54G06V10/40G06N3/045
Inventor 李琳辉李佳骏连静周雅夫钱波苏兵
Owner DALIAN UNIV OF TECH
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