基于卷积神经网络的矿工安全帽佩戴识别研究

Identification Research on the Miner's Safety Helmet Wear Based on Convolutional Neural Network

为了解决矿山安全监控监测主要靠人工对视频数据进行识别而存在的诸多人为因素限制的问题,通过构建卷积神经网络实现计算机智能识别矿工安全帽的佩戴,在不增加任何辅助装置的条件下实现矿工安全着装智能识别。从视频数据中提取图像,通过对图像进行旋转、偏移、剪切等预处理,将图像分为矿山背景、戴安全帽的矿工和不戴安全帽的矿工3类。通过构建3种不同深度层次的卷积神经网络进行实验对比,"4个卷积层+3个池化层+3个全连接层"组成的深层网络识别准确率较高,达到91.2%。实验表明利用卷积神经网络可以较好地实现对矿工是否正确佩戴安全帽的智能识别。研究方法为人工智能应用于矿山的安全监控、安全行为及安全状态的智能识别研究提供借鉴。

In order to solve human factors restrictions of mine safety monitoring,which relied on manual monitoring of video data to identify risk factors,a method of construction of deep convolution network to identity whether miners wear helmets without adding any auxiliary device.Images was extracted from video data,which was divided into three categories：background,miners wearing helmets and miners without helmets,through rotated offset and sheared for image.The experimental companison is made by constructing three different levels of convolutional neural networks.Experiment shows that deep convolution network which was developed by＂4 convolution layers＋3 pooling layers＋3 fully connected layers＂has a highest recognition rate,reached 91.2%.Convolution neural network can achieve intelligent identification of miners dress safety.Research show that intelligent recognition of mine has an important reference for safety monitoring,safe behavior and security status.