Food safety index monitoring method based on deep learning

A food safety and deep learning technology, applied in the field of food safety indicator monitoring based on deep learning, can solve problems such as high labor costs, poor visualization, and small scope of activities of monitoring personnel

Pending Publication Date: 2021-07-30
贵州贵科大数据有限责任公司 +1
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

This method mainly has the following disadvantages: traditional manual monitoring has high labor costs, and generally the scope of monitoring personnel's activities is small, easy to omit, poor visualization, and problems cannot be traced back

Method used

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  • Food safety index monitoring method based on deep learning
  • Food safety index monitoring method based on deep learning
  • Food safety index monitoring method based on deep learning

Examples

Experimental program
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Effect test

Embodiment 1

[0039] Example 1. A food safety indicator monitoring method based on deep learning, see figure 1 , follow the steps below,

[0040] a. Label the data collected on site according to the food safety indicators, and construct the scene data set;

[0041] b. Use the scene data set to train the Faster-RCNN deep learning model to build a target recognition model;

[0042] c. Use the purpose identification model to monitor food safety indicators on the real-time data on site.

[0043] In step a, the on-site collection data is video data.

[0044] In step a, data labeling, see figure 2 , the steps are as follows:

[0045] a1. Video segmentation: Divide a video data into more than one 2000-frame segment, and select 10-20 intervals as acquisition segments;

[0046] a2. Video publishing: reorganize the collected clips into video clips, and import them into vatic’s internal database to generate video clips—sequence pairs of web page links; Vatic is a semi-automatic video tagging to...

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Abstract

The invention discloses a food safety index monitoring method based on deep learning. The method comprises the following steps: a, carrying out data labeling on field collected data according to food safety indexes, and constructing a scene data set; b, training a Faster-RCNN deep learning model by using the scene data set, and constructing a target recognition model; and c, performing food safety index monitoring on the field real-time data by using the target identification model. The method has the characteristics of wide monitoring range, high monitoring efficiency and good traceability.

Description

technical field [0001] The invention relates to the technical field of food safety monitoring, in particular to a method for monitoring food safety indicators based on deep learning. Background technique [0002] With the development of economy, people are paying more and more attention to food safety. In food safety, the safety production link of food is the top priority. At present, the detection of relevant indicators of food safety production, such as: workers' clothing compliance, food additive placement, live detection of mice and other issues, mainly uses manual monitoring and control. This method mainly has the following disadvantages: traditional manual monitoring has high labor costs, and generally the scope of monitoring personnel's activities is small, easy to omit, poor visualization, and problems cannot be traced back. Contents of the invention [0003] The purpose of the present invention is to provide a method for monitoring food safety indicators based o...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06N3/04G06N3/08G06Q30/00
CPCG06N3/08G06Q30/018G06V20/49G06V20/41G06V20/46G06V10/25G06N3/045
Inventor 陈恺王雅洁杨冰杨鑫张成梅郝淼于杰杨红黄伟王明慧秦梅元
Owner 贵州贵科大数据有限责任公司
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