A road target detection method and device based on convolutional neural network

A convolutional neural network, target detection technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as insensitivity to small-scale object detection

Active Publication Date: 2022-05-03
SHENZHEN UNIV
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Problems solved by technology

[0004] In order to solve the problem that the target detection method based on the deep neural network is not sensitive to the detection of small-scale objects in the prior art, the embodiment of the application provides a road target detection method and device based on the convolutional neural network, and improves the accuracy of the detection method. The accuracy of small-scale object detection can effectively detect 9 common road traffic objects such as private cars, buses, trucks, pedestrians, motorcycles, bicycles, riders, traffic lights and traffic signs on the road environment

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  • A road target detection method and device based on convolutional neural network
  • A road target detection method and device based on convolutional neural network
  • A road target detection method and device based on convolutional neural network

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[0049]In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0050] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude one or more other Presence or addition of features, wholes, steps, operations, elements, components and / or collections thereof.

[0051] It should ...

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Abstract

This application is applicable to the field of image processing technology, and provides a method and device for target detection based on convolutional neural networks. The method includes: importing a real-time image into a target detection network, and outputting the target object contained in the real-time image; the target detection The network includes a convolutional layer, an inverse convolutional layer, a feature enhancement block, a feature fusion block, a first regressor and a second regressor. The method of the present application can solve the problem that the object detection method based on the convolutional neural network in the prior art is not sensitive to small-scale object detection.

Description

technical field [0001] The present application belongs to the technical field of image processing, and in particular relates to a road target detection method and device based on a convolutional neural network. Background technique [0002] For autonomous vehicles, the visual perception unit is of great significance for autonomous vehicles to perceive the surrounding environment. Among them, the task of road object detection is the most basic and crucial task in the visual perception unit of autonomous vehicles. For the driving scene pictures captured by the on-board camera, most of the objects in it are small in scale. Effective recognition of these small objects will further improve the safe driving of autonomous vehicles. Therefore, a road target detection suitable for autonomous driving The method should have high accuracy and high efficiency, and have a strong ability to detect small-scale targets. [0003] In recent years, the detection accuracy of object detection me...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/58G06V10/46G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/56G06V10/462G06V2201/07G06N3/045G06F18/253
Inventor 李国法杨一帆赖伟鉴朱方平陈耀昱曲行达
Owner SHENZHEN UNIV
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