Method for improving instance segmentation based on unmanned driving technology

A technology of unmanned driving and technology, applied in the direction of reasoning methods, neural learning methods, instruments, etc., to achieve the effect of low cost, improved efficiency, and improved segmentation quality

Inactive Publication Date: 2019-09-24
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, in deep learning, only single-threaded tasks can be completed in a framework. In recent years, with the development of deep learning and computer vision, it is increasingly required to achieve multi-task integration in deep neural networks, na

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  • Method for improving instance segmentation based on unmanned driving technology
  • Method for improving instance segmentation based on unmanned driving technology
  • Method for improving instance segmentation based on unmanned driving technology

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Embodiment Construction

[0045] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0046] Such as Figure 1-6 As shown, the present invention is a multi-threaded task set improved based on MASK R-CNN. We initially wanted to use semantic segmentation. We can first understand semantic segmentation. Semantic segmentation is based on pixel-level segmentation. By inputting a picture into the semantic segmentation framework, it will be optimized by FCN and CRF in turn, and finally output. Semantic segmentation map where pixels are segmented. For the specific framework of semantic segmentation, we can refer to the attached figure 2 .

[0047] However, semantic segmentation has a relatively big defect, that is, the result of semantic segmentation does not distinguish between the same type of things, that is, when facin...

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Abstract

The invention discloses a method for improving instance segmentation based on an unmanned driving technology. According to the method, targets are detected and classified on the basis of the Faster R-CNN based on the MASK R-CNN, and then instance segmentation is achieved through FCN feature coarse extraction and CRF optimization output. The method comprises the following specific implementation steps: step 1, classifying targets by using a partial supervision method; step 2, adopting depth separable convolution in the semantic segmentation convolution process to obtain features; and step 3, performing feature fusion optimization on the features obtained by the convolutional layer, introducing semantic information in a low layer, and introducing spatial information in a high layer. According to the method, relatively good target detection and classification results are established at relatively low cost. The method adopts depth separable convolution, thus improving the precision of a segmentation result and the efficiency of a computer, and reducing time loss.

Description

technical field [0001] The invention belongs to a typical problem of computer vision, image recognition and image classification. In particular, the present invention intends to make some improvements to instance segmentation based on unmanned driving technology. The present invention is based on data classification and a method of improving the structure of FCN. [0002] technical background [0003] In recent years, unmanned driving technology has developed rapidly. Unmanned vehicles are intelligent vehicles that sense the road environment through on-board sensing systems, automatically plan driving routes, and control vehicles to reach predetermined targets. In unmanned driving technology, how to effectively, quickly and accurately identify objects and pedestrians in front of the vehicle is undoubtedly a big obstacle to the development of current unmanned driving technology. If it cannot be accurately identified and reacted in a short time, Then unmanned driving technolog...

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Application Information

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IPC IPC(8): G06K9/62G06K9/00G06N3/04G06N3/08G06N5/04
CPCG06N3/08G06N5/04G06V20/56G06N3/045G06F18/241G06F18/214
Inventor 颜成钢黄继昊刘启钦孙垚棋张继勇张勇东
Owner HANGZHOU DIANZI UNIV
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