基于安全帽佩戴检测的矿山人员违规行为研究

doi:10.16180/j.cnki.issn1007-7820.2019.09.006

Abstract

Abstract:

In order to solve the problem of the miner's helmet-wearing recognition, this paper proposed a method of detecting the safety helmet based rule-violated activity. It was based on the face recognition and helmet detection. Firstly, detect the human faced in the picture and identify them, and then detect whether the person wears a safety helmet or not with the Convolutional Neural Network approach. This paper compared the depth learning method with the traditional method in different conditions in the experiments.The experimental results showed that the robustness of the depth learning based safety helmet detection method was stronger than the traditional method. The recognition rate of the depth learning method was higher than that of the traditional approach under different conditions. The deep learning method achieved an average recognition rate of 97% and the average running time was less than 1/7 of that of the traditional approach.

Key words: safety helmet detection, identity recognition, rule-violated activity, deep learning, accuracy and efficiency, average time

CLC Number:

TN399

TONG Zeyou,FENG Shimin,HOU Xiaoqing,DING Enjie. Recognition of Underground Miners’ Rule-Violated Behavior Based on Safety Helmet Detection[J].Electronic Science and Technology, 2019, 32(9): 26-31.

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References 17

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类型	kernel尺寸/步长	输入尺寸
卷积	3×3/2	416×416×3
池化	3×3/1	207×207×32
卷积	3×3/1	103×103×64
池化	3×3/1	101×101×64
卷积	3×3/2	99×99×64
池化	3×3/1	45×45×64
线性logits	1×1×64
softmax	分类输出	1×1×10

实验次数	传统方法		深度学习方法
实验次数	50	100	50	100
安全帽正面	94%	95%	98%	97%
安全帽侧面	12%	10%	96.6%	98%
光线弱(200 lux)	92%	90%	98%	97%
光线强(3 000 lux)	89%	98%	98%
距离2 m	96%	94%	96%	97%
距离4 m	26%	24%	94%	96%

实验次数	传统方法		深度学习方法
实验次数	50	100	50	100
正常光照条件	92%	90%	86%	85%
光线弱(200 lux)	42%	39%	25%	30%
光线强(3 000 lux)	56%	60%	24%	20%
距离2 m	94%	91%	84%	86%
距离4 m	20%	23%	62%	65%

次数方法	50	100
传统方法	12 338 ms	12 260 ms
深度学习方法	1 703 ms	1 681 ms

Recognition of Underground Miners’ Rule-Violated Behavior Based on Safety Helmet Detection

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