A visual scene recognition system and method based on deep neural network

A deep neural network and scene recognition technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as lack of strong correlation and poor effect, and achieve scalability and strong robustness , Enrich the effect of scene classification results

Active Publication Date: 2020-08-18
ZHEJIANG LEAPMOTOR TECH CO LTD
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AI Technical Summary

Problems solved by technology

[0004] An object of the present invention is to solve the existing weather judgments in the above-mentioned prior art mostly based on the system clock, and weather judgments based on auxiliary sensors such as rainfall. Such methods do not have strong correlation with the visual system input itself, so in some boundary In order to solve the problem of poor situation effect, a visual scene recognition system and method based on deep neural network is provided

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  • A visual scene recognition system and method based on deep neural network
  • A visual scene recognition system and method based on deep neural network

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Embodiment Construction

[0021] The technical solutions of the present invention will be further specifically described below through specific embodiments and in conjunction with the accompanying drawings.

[0022] Based on the visual input of the vehicle-mounted camera, the present invention judges the driving environment of the vehicle and the scene state of the visual system, and provides basic configuration information for related algorithms. etc. provide a visual scene recognition system based on deep neural network, such as figure 1 As shown, it includes: a vehicle vision system, which is used to collect vehicle front-view visual images; an offline training module, which is used to collect samples from the vehicle front-view visual images collected by the vehicle vision system, and mark them, generate sample labels, and perform The neural network parameters are trained step by step to obtain a deep convolutional neural network; the online analysis module is based on the input of the vehicle visi...

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Abstract

A visual scene recognition system based on a deep neural network, including: a vehicle-mounted vision system, which is used to collect vehicle forward-looking visual images; an offline training module, which is used to use a deep convolutional neural network to perform visual input on vehicles collected from the vehicle-mounted vision system Sample collection is performed in the forward-looking visual image, labeling is performed, sample labels are generated, and neural network parameters are trained step by step; the deep convolutional neural network is composed of a three-branch classification network that shares two layers of shallow convolutional features. and training task training network parameters; and, the online analysis module adopts network compression and time-sharing parallel analysis strategy to carry out real-time scene analysis on the samples trained by the offline training module, and outputs the time, weather and Abnormal state of the scene.

Description

technical field [0001] The invention relates to the field of vehicle safety, in particular to a visual scene recognition system and method based on a deep neural network. Background technique [0002] Intelligentization has now become an important development direction of the automotive industry. The perception technology based on visual sensors is becoming more and more mature, and its application in the field of vehicle active safety is becoming more and more extensive. For the vision system, for different road environments, from image acquisition to application layer algorithms, corresponding adjustments will be made from the parameters and strategy levels. Therefore, accurate recognition of scene information in visual input has strong application value and significance, and, The anomaly diagnosis of the visual scene can further enhance the robustness and fault tolerance of the system. [0003] Most of the existing vision systems do not have such perfect basic algorithms...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/10G06N3/045
Inventor 缪其恒王江明许炜
Owner ZHEJIANG LEAPMOTOR TECH CO LTD
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