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Safety helmet wearing detection method in complex scene

A technology of complex scenes and detection methods, applied in the field of deep learning and target detection, can solve the problems of small target factors, small size of individuals on the screen, mutual occlusion, etc., to improve the detection effect, ease the labeling work, and reduce the effect of information loss.

Pending Publication Date: 2022-04-29
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At the same time, the inspection of helmets on the subway construction site will encounter challenges such as scale transformation, perspective distortion, and small target factors.
If the distance from the camera is different, there will be differences in the size of the individuals to be detected on the screen; in some scenes, the construction personnel are dense, and there will be mutual occlusion; due to limited conditions, the camera needs to be placed far away from the construction site. It will result in smaller individual sizes on the screen; at the same time, some construction sites have complex backgrounds, which will also affect the detection of wearing helmets
The existence of these scenarios greatly limits the performance of detection algorithms

Method used

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  • Safety helmet wearing detection method in complex scene
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  • Safety helmet wearing detection method in complex scene

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0073] A safety helmet wearing detection method in a complex scene, comprising the following steps:

[0074] Step A, according to the characteristics of the large size change of the detection target in the picture in complex scenes, design four feature scales for helmet detection;

[0075] On the basis of the YOLO v5 network detection of the neck and head, the fourth detection scale with a smaller receptive field is added to enhance the detection effect on small targets. The four detection scales are 13×13, 26×26, and 52 respectively. ×52,104×104, compared with the original three scales, it has a larger scale detection range;

[0076] During YOLO v5 training, the objective function of its bounding box regression and the real value B gt It is related to the predicted value B, and its calculation is shown in formula (1),

[0077]

[0078] where d is the center c of the true value gt and the distance between the center c of the predicted frame, l is the diagonal length of t...

Embodiment 2

[0130] This embodiment adopts the safety-helmet-wearing-dataset of network open source hard hat data set and data expansion picture, before targeted data expansion (DA), comprise altogether 9047 normal hats wearing hard hats and 9082 hats not wearing hard hats For the negative class person, the two categories are randomly divided into training set and test set according to the ratio of 8:2 to train and test the network. After targeted data expansion, the number of person classes increases to 35531, and the test set remains unchanged. . In order to verify the effectiveness of the changes proposed by the present invention, in this embodiment, the original YOLO v5 network is selected as the baseline, the fourth detection scale (FS) is added in turn, the attention mechanism (SB) is introduced, and the targeted data expansion ( DA), transfer learning (PT), tested on the same test set, and evaluated the model from two aspects of accuracy and speed. The experimental environment is sh...

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Abstract

The invention discloses a safety helmet wearing detection method based on multi-scale features, and the method comprises the steps: introducing an attention mechanism into a YOLO v5 network backbone part, and reducing the loss of effective information in a network during transmission; a fourth detection scale 104 * 104 is added to the neck and the head of the YOLO v5 network, so that the detection capability of a small target is enhanced; after a CSPDarkNet53 model is pre-trained on a large data set, the feature extraction capability of the CSPDarkNet53 model is transferred and learned to a safety helmet wearing detection model, and the problem that the data set is insufficient is relieved; inferring a human body boundary frame according to the safety helmet wearing detection frame, extracting skeleton key points of a person who does not wear the safety helmet, designing a gait recognition module, and recognizing the identity of the person who does not wear the safety helmet; according to the method, the accuracy of the safety helmet wearing detection model is improved by using the multi-scale features in a complex scene, and the identity of a person not wearing the safety helmet is confirmed by fusing the gait recognition algorithm.

Description

technical field [0001] The invention relates to the technical field of deep learning and target detection, in particular to a multi-scale feature-based helmet wearing detection method and its application. Background technique [0002] With the development of urbanization, a large number of infrastructures such as subways need to be built, and the safety of construction sites has attracted more and more attention. Safety helmets are one of the effective personal protective equipment to reduce workers' injuries when they fall or are hit by falling objects, and wearing safety helmets on construction sites is a legal requirement all over the world. However, due to the discomfort caused by wearing a helmet and the lack of safety awareness of workers, workers often take off their helmets unconsciously. Therefore, long-term detection of whether workers wear safety helmets is crucial to their safe production and can improve the level of safety management. The traditional construct...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V40/10G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24
Inventor 韩锟曾向东肖友刚李蔚
Owner CENT SOUTH UNIV