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Safety helmet detection method oriented to actual production

A detection method and helmet technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of slow detection speed and low discrimination accuracy, and achieve improved accuracy, accurate detection area, fast, accurate and real-time Detection effect

Pending Publication Date: 2021-07-23
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems of slow detection speed and low discrimination accuracy existing in the existing helmet detection technology, the present invention provides a production-oriented helmet detection method, that is, the two-step method of head detection and positioning-hard hat reclassification

Method used

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  • Safety helmet detection method oriented to actual production

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Embodiment Construction

[0027] The present invention will be further described below in conjunction with the accompanying drawings.

[0028] refer to figure 1 , a production-oriented safety helmet detection method. In this implementation case, the head image of a person wearing a safety helmet is used as a positive sample, and the head image of a person not wearing a safety helmet is used as a negative sample. Since the manual collection of sample images has the characteristics of long cycle, small quantity, and high cost, it cannot fully meet the large number of sample images required for model training. Therefore, other methods are needed to enhance the sample images, which can improve the recognition rate of model training to a certain extent. .

[0029] The detection method comprises the following steps:

[0030] Step 1. Initial training data collection and labeling, the process is as follows:

[0031] Step 1.1: Obtain a large number of pictures of wearing helmets and not wearing helmets in th...

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Abstract

The invention discloses a production reality-oriented safety helmet detection method. The method comprises the following steps of 1, collecting and marking initial training data; 2, training the data set Cf obtained in the step 1 to obtain an FCOS target detection model Mf; 3, training the data set Cr obtained in the step 1 to obtain a Resnet18 target classification model Mr; and 4, inputting an original image, and carrying out production workshop image detection by using a head detection positioning-safety helmet reclassification two-step method. According to the robust safety helmet detection system suitable for the complex production scene, the labor cost is greatly reduced, and the practical application value of the method is improved; according to the method, FCOS and Resnet18 are subjected to cascade detection, so that the problem of insufficient training samples is effectively solved; according to the method, while the target position is quickly detected, the target features are accurately extracted, and accurate target classification is realized.

Description

technical field [0001] The invention relates to big data processing and analysis in the field of computer vision, in particular to safety helmet detection in a complex production environment, specifically a method for judging whether a person wears a safety helmet through the two-step method of head detection and positioning-hard hat reclassification, belonging to field of machine learning and machine vision. Background technique [0002] As the most basic personal protective equipment for workers, hard hats are of great significance to the life safety of workers. However, some operators lack safety awareness, and violations of not wearing safety helmets often occur. Hard hat detection has become an important technology for building production safety video surveillance, and it is widely demanded in actual scenarios such as coal mines, substations, and construction sites. Traditional detection algorithms rely on a large amount of prior knowledge and are highly subjective. T...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/52G06V10/25G06V2201/07G06N3/044G06N3/045G06F18/241G06F18/214
Inventor 王雯卿崔滢倪雨婷金子钰蒋焘
Owner ZHEJIANG UNIV OF TECH
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