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Method and system for identifying illegal hazardous chemicals transport vehicles based on convolutional neural network

A convolutional neural network and transportation vehicle technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as high labor costs, time-consuming, and unrealizable problems, and achieve reduced labor costs and less time-consuming , the effect of improving flexibility and portability

Active Publication Date: 2021-11-19
智广海联(天津)大数据技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

If all roads are fully manually supervised, it will take a long time and the labor cost will be high, making it impossible to achieve

Method used

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  • Method and system for identifying illegal hazardous chemicals transport vehicles based on convolutional neural network
  • Method and system for identifying illegal hazardous chemicals transport vehicles based on convolutional neural network
  • Method and system for identifying illegal hazardous chemicals transport vehicles based on convolutional neural network

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Experimental program
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Embodiment 1

[0077] refer to figure 1 , which is a convolutional neural network-based identification method for illegal hazardous chemicals transport vehicles disclosed in the present invention, comprising the following steps:

[0078] S100. Obtain training data, and then perform data standardization processing on the training data. The training data is an initial labeled image library, and the initial labeled image library includes labeled hazardous chemical pictures, hazardous chemical icons, and standard hazardous chemical transport vehicles. examples.

[0079] Wherein, step S100 further includes:

[0080] S110, image grayscale processing: Gaussian blur is performed on the image of the training data to reduce image noise, and then weighted grayscale processing is performed on the R, G, and B channels of the image. In this embodiment, the weights of R, G, and B are respectively selected 0.299, 0.587, 0.114.

[0081] S120. Image edge analysis: use the canny algorithm to detect edges in t...

Embodiment 2

[0120] refer to Image 6 , an illegal hazardous chemicals vehicle identification system based on a convolutional neural network, comprising a first data input processing unit 401, a convolution operation unit 402, a second data input processing unit 403 and a decision output unit 404, wherein the convolution operation unit 402 It is connected to the first data input processing unit 401 , the second data input processing unit 403 is connected to the convolution operation unit 402 , and the decision output unit 404 is connected to the second data input processing unit 403 .

[0121] The first data input processing unit 401 is used for acquiring training data and performing standardization processing on the training data, where the training data is an initial labeled image library.

[0122] The convolution operation unit 402 includes a convolutional neural network calculation model, which is used to perform convolution operations on the processed training data, and obtain the fin...

Embodiment 3

[0127] refer to Figure 7 , is an embodiment of an electronic device provided in the present invention, and the electronic device includes: a processor 501 , and a memory 502 configured to store executable instructions of the processor 501 . Optionally, it may also include: a communication interface 503, configured to communicate with other devices.

[0128] Wherein, the processor 501 is configured to execute the method corresponding to the foregoing method embodiment by executing the executable instruction, and the specific implementation process may refer to the foregoing method embodiment, and details are not repeated here.

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PUM

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Abstract

The invention discloses a method for identifying illegal hazardous chemicals transport vehicles based on a convolutional neural network, comprising the following steps: S100, acquiring training data, and performing data standardization processing; S200, performing convolution operations using a convolutional neural network calculation model, and obtaining training data A good calculation model and the final labeled image library; S300. Obtain the local hazardous chemical vehicle database and the data to be identified, calculate and identify the data to be identified, compare and judge with the final labeled image library, and output the characteristics of hazardous chemical vehicles or suspected hazardous chemical vehicles According to the characteristics of the dangerous chemical vehicle, it is then judged with the local hazardous chemical vehicle database to distinguish the standard hazardous chemical transport vehicle from the illegal hazardous chemical transport vehicle. The invention also discloses an identification system for illegal hazardous chemical transport vehicles based on convolutional neural network, which can identify standard hazardous chemical transport vehicles and illegal hazardous chemical transport vehicles in traffic image streams by using image recognition and convolutional neural network calculation models Transporting vehicles takes less time and reduces labor costs.

Description

technical field [0001] The invention relates to the technical field of intelligent identification of hazardous chemical logistics, in particular to a method and system for identifying illegal hazardous chemical transport vehicles based on convolutional neural networks. Specifically, it belongs to G06Q10 / 08 in the IPC classification. Background technique [0002] With the rapid development of my country's economy, the volume of road and logistics transportation continues to increase, and traffic safety and smooth flow have become the top priority, especially the storage and logistics of hazardous chemicals. Due to its characteristics of flammability, explosion, poisoning, and pollution, accidents during transportation will cause huge losses. For the controlled transportation of hazardous chemicals, the coordinated operation of multiple departments is required, and real-time information exchange is required between these departments, and specific information should be display...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06T7/12G06T7/13G06T5/00G06T3/40G06N3/04G06N3/08
CPCG06T7/12G06T7/13G06T3/4007G06N3/084G06T2207/20081G06T2207/20084G06T2207/20192G06T2207/30248G06N3/045G06F18/214G06F18/24G06T5/90G06T5/70
Inventor 宫跃峰任衡王阳王璐
Owner 智广海联(天津)大数据技术有限公司