Converged network lane line detection method based on attention mechanism and terminal equipment

A technology for lane line detection and fusion network, which is applied in the field of fusion network lane line detection method and terminal equipment based on attention mechanism

Active Publication Date: 2020-11-17
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Compared with single information, fusion information is quite different, but most of the existing attention models are input from natural image feature maps, and it is difficult to have a good performance on the fusion information of point cloud data and natural image data.

Method used

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  • Converged network lane line detection method based on attention mechanism and terminal equipment
  • Converged network lane line detection method based on attention mechanism and terminal equipment
  • Converged network lane line detection method based on attention mechanism and terminal equipment

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Experimental program
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Embodiment 2

[0140] Embodiment 2 of the present invention discloses a fusion network lane line detection system based on attention mechanism, including:

[0141] The data collection module is used for synchronous collection of natural images and point cloud data of the road surface;

[0142] The lane monitoring module is used to input the natural image and point cloud data into the pre-established and trained fusion network, and output the lane detection result; the fusion network adopts adding timing frames and attention mechanism to fuse the point cloud data and natural image information deal with.

Embodiment 3

[0144] Such as Figure 13 As shown, a terminal device provided by Embodiment 3 of the present invention includes: at least one processor 301 , a memory 302 , at least one network interface 303 and a user interface 304 . The various components are coupled together via a bus system 305 . It can be understood that the bus system 305 is used to realize connection and communication between these components. In addition to the data bus, the bus system 305 also includes a power bus, a control bus and a status signal bus. However, for clarity of illustration, the various buses are labeled as bus system 305 in the figure.

[0145] Wherein, the user interface 304 may include a display, a keyboard or a pointing device (for example, a mouse, a track ball (track ball), a touch panel or a touch screen, and the like.

[0146] It can be understood that the memory 302 in the embodiment of the present disclosure may be a volatile memory or a non-volatile memory, or may include both volatile ...

Embodiment 1

[0151] The method in Embodiment 1 may be applied to the processor 301 or implemented by the processor 301 . The processor 301 may be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 301 or instructions in the form of software. The above-mentioned processor 301 may be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable GateArray, FPGA) or other programmable Logic devices, discrete gate or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in Embodiment 1 can be realized or executed. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the...

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Abstract

The invention discloses a converged network lane line detection method based on an attention mechanism and terminal equipment. The method comprises the following steps: synchronously acquiring a natural image and point cloud data of a road surface; inputting the natural image and the point cloud data into a pre-established and trained fusion network, and outputting a lane detection result, whereinthe fusion network performs information fusion processing on the point cloud data and the natural image by adopting an adding time sequence frame and an attention mechanism. According to the invention, continuous frames are used to improve the performance of the detection network so as to deal with complex situations such as mark loss and vehicle occlusion; and by splicing the low-dimensional features and the high-dimensional features through Skip Connection, image detail information which is continuously lost along with the increase of the network depth is made up, and the image is reduced by using Decoder to obtain a final result. The fusion network greatly improves the detection performance of the lane lines and can perform detection in various complex environments.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to an attention mechanism-based fusion network lane line detection method and a terminal device. Background technique [0002] The lane line detection method is mainly used in the field of automatic driving. The recognition of the lane line can determine the positional relationship between the vehicle and the current lane, and then judge and realize the following functions: (1) lane departure warning, (2) lane keeping assistance, (3) Lane Centering Assist, (4) Automatic Lane Change Assist. The driver assistance system provides the decision-making basis for the automatic driving control module through the lane line detection results, and the lane line detection results will determine the performance of the driver assistance system to a large extent. [0003] At present, lane line detection has become a research and application hotspot of scholars at home and abro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/588G06N3/044G06N3/045G06F18/253G06V10/82G06V10/454G06V10/803G06N3/088G06N3/048G06V10/34G06F18/214G06F18/251
Inventor 张新钰李志伟李骏宫彦高鑫刘华平
Owner TSINGHUA UNIV
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