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A multi-module collaborative object recognition system and method based on deep learning

An object recognition and deep learning technology, applied in the field of deep learning, can solve the problem that the 4G network bandwidth cannot meet the real-time transmission of high-quality video images, and the real-time performance of object recognition cannot be guaranteed, so as to shorten the image processing time, avoid time delay, reduce The effect of CPU usage

Active Publication Date: 2021-12-10
山东奥邦交通设施工程有限公司
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Problems solved by technology

[0005] However, the inventors found that with the increase of video image quality, the existing 5G network infrastructure does not cover all, and the 4G network bandwidth cannot meet the real real-time transmission of high-quality video images. The data is further processed by computer vision for object recognition, and the real-time performance of object recognition cannot be guaranteed.

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  • A multi-module collaborative object recognition system and method based on deep learning

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Embodiment 1

[0052] refer to figure 1The multi-module cooperative object recognition system based on deep learning in this embodiment includes a video input module, a video processing subsystem module, an intelligent video engine module, a neural network acceleration engine module, and a video graphics subsystem module that are integrated and work together and video output modules.

[0053] In the specific implementation, in order to ensure the accuracy of later data processing, the video input module, video processing subsystem module, intelligent video engine module, neural network acceleration engine module, video graphics subsystem module and video output module are all Start and perform initialization operations at the same time.

[0054] During the initialization operation, the initialization of the neural network acceleration engine module includes loading a trained neural network model in a specific format. Before loading, it is necessary to convert the format of the trained neur...

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Abstract

The invention belongs to the technical field of deep learning, and provides a multi-module cooperative object recognition system and method based on deep learning. Among them, the system includes a video input module, a video processing subsystem module, an intelligent video engine module, a neural network acceleration engine module, a video graphics subsystem module and a video output module that are integrated and work together. It uses multi-module collaboration to realize real-time recognition of objects, solves the problem of uploading camera images to the server for recognition processing, avoids the time delay caused by network delay or network bandwidth limitation, and realizes real real-time recognition.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and in particular relates to a multi-module cooperative object recognition system and method based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Visual information processing is to build an intelligent system that simulates human visual ability based on external perception data and to judge and recognize targets. Among them, object recognition is the basis of visual information processing technology. With the popularization of computers and smart terminals and the rapid development of the Internet, the rapid expansion of image and video big data applications poses challenges to object recognition technology. The current object recognition technology should have the characteristics of high efficiency, high performance and even intellig...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/04G06N3/08G06F18/241
Inventor 奚照明杨哲邵强梁昭蔡达张辉马琳
Owner 山东奥邦交通设施工程有限公司