Safety protection equipment identification method based on autonomous learning strategy and storage medium

Abstract

The invention belongs to the technical field of security and protection monitoring, and particularly relates to a safety protection equipment identification method based on an autonomous learning strategy and storage medium. The method comprises the following steps: S1, collecting a training picture set; S2, preprocessing the training picture set; S3, training the constructed deep network model according to the training picture set; S4, inputting a to-be-detected picture set into the deep network model for identification to obtain an identification result set; S5, classifying the recognition result set into a successful recognition set and an unrecognizable set; S6, outputting an identification success set; and S7, taking the to-be-detected picture set corresponding to the unrecognizable set as a new training picture set, and skipping to S2 to continue execution. The method does not need to occupy a large number of artificial resources and computing resources, generates new training samples in a semi-automatic manner, and is suitable for different complex scenes. By introducing the weight of the features and a label smoothing mechanism, the accuracy of obtaining the network features is ensured, and overfitting is effectively prevented.

Description

technical field [0001] The invention belongs to the technical field of security monitoring, and in particular relates to a method for identifying security protection equipment based on an autonomous learning strategy and a storage medium. Background technique [0002] The safety behavior of wearing safety helmets and other protective equipment is an effective means to prevent unsafe accidents in construction sites and protect construction workers. [0003] The traditional protective equipment wear detection method uses manual detection, which has defects such as time-consuming, high labor costs, and high false detection rate. With the development of computer technology, more and more construction sites have gradually introduced intelligent detection systems, but most of the intelligent detection systems Technologies are based on traditional machine vision techniques, including: [0004] 1) A method based on traditional digital image processing plus machine learning. Such m...

AI Technical Summary

Problems solved by technology

Compared with traditional machine learning methods, deep learning generally does not require various feature engineering, and is more adaptable and generalizable, and can obtain more accurate target detection results under a large number of sample training. It has been widely used in recent years, but Model reasoning has high requirements on sample resources, which cannot be used in different resource scenarios

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