Method for identifying unsafe behaviors of power plant workers

A technology of safe behavior and staff, applied in the field of computer vision, can solve problems such as the powerlessness of deeper semantic target tasks, and achieve the effects of saving manpower, ensuring safe production, and low price

Inactive Publication Date: 2021-07-13
济南奔腾时代电力科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the technologies currently used are all direct detection of task targets, and are powerless for deeper semantic target tasks.

Method used

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  • Method for identifying unsafe behaviors of power plant workers
  • Method for identifying unsafe behaviors of power plant workers
  • Method for identifying unsafe behaviors of power plant workers

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Embodiment Construction

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0030] Please refer to the attached Figures 1 to 4 , a method for identifying unsafe behaviors of power plant staff provided by an embodiment of the present invention is specifically:

[0031] S1: Preset several unsafe behaviors of staff, and use the information of video images corresponding to the unsafe behaviors as a data set.

[0032] It includes training the convolutional neural network. The training method is to mark out the following information of th...

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Abstract

The invention belongs to the technical field of computer vision, and particularly relates to a method for identifying unsafe behaviors of power plant workers. The method specifically comprises the steps of presetting a plurality of working personnel unsafe behaviors, obtaining a field video image, analyzing the video image through a convolutional neural network of an improved YOLOv4 algorithm, and identifying the unsafe behaviors, including unsafe behavior analysis of a single-frame image, unsafe behavior analysis of a video sequence, and unsafe behavior analysis of a specific task and a scene. The method has the beneficial effects that the method is based on a computer vision technology, and unsafe behaviors are recognized in combination with unsafe behavior semantic information. Compared with an existing scheme, the method has the advantages of low price, high efficiency, real-time detection and no dead angle leakage, and a large amount of manpower, material resources and financial resources are saved. The method can be popularized and applied to unsafe behavior identification scenes in all industrial fields.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for identifying unsafe behaviors of power plant staff. Background technique [0002] The traditional identification of unsafe human behavior is carried out by manual on-site inspection or video viewing in the monitoring room. Although the accuracy of this method is very high, the efficiency is extremely low and the coverage is small. It is difficult to identify all unsafe behaviors comprehensively and in real time. Therefore, unsafe behaviors in power plants still occur from time to time. [0003] In recent years, with the development of computer vision technology, intrusion detection technology has begun to be applied to unsafe behavior recognition. The characteristic of intrusion detection technology is to determine whether there is a target by calculating the frame difference of two consecutive video frames. This method is more effective for movi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/32G06N3/04
CPCG06V40/20G06V40/10G06V20/40G06V10/25G06N3/045
Inventor 唐守伟张传昀张超王新刘继勇刘海瑞
Owner 济南奔腾时代电力科技有限公司
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