基于MobileNet-SSD的安全帽佩戴检测算法  

Detection Algorithm of Safety Helmet Wear Based on MobileNet-SSD

作　　者：徐先峰[1] 赵万福 邹浩泉 张丽 潘卓毅 XU Xianfeng;ZHAO Wanfu;ZOU Haoquan;ZHANG Li;PAN Zhuoyi(School of Electronics and Control Engineering,Chang'an University,Xi'an 710064,China)

机构地区：[1]长安大学电子与控制工程学院,西安710064

出　　处：《计算机工程》2021年第10期298-305,313,共9页Computer Engineering

基　　金：国家自然科学基金(61201407,71971029);陕西省自然科学基础研究计划(2016JQ5103,2019GY-002);长安大学中央高校基本科研业务费专项资金(300102328202);西安市智慧高速公路信息融合与控制重点实验室项目(ZD13CG46)。

摘　　要：针对真实场景下安全帽佩戴检测面临的背景复杂、干扰性强、待检测目标较小等问题,在SSD算法的基础上,提出改进的MobileNet-SSD算法。通过引入轻量型网络MobileNet并构建MobileNet-SSD算法提高检测速度,采用迁移学习策略克服模型训练困难问题。从施工相关视频中获取真实环境下的安全帽样本构建样本集,以解决当前安全帽数据集规模较小、网络难以充分拟合特征的问题。实验结果表明,MobileNet-SSD算法在损失很小精度的情况下,相较于SSD算法,检测速度提高了10.2倍。The existing methods for checking the wear of safety helmets suffer from complex background and strong interference,and display poor performance on small targets.To address the problem,an improved SSD algorithm is proposed for detecting the wear of safety helmets.The algorithm employs the lightweight MobileNet to construct the MobileNet-SSD algorithm,which improves the detection speed.Then the transfer learning strategy is introduced to address the difficulties in model training.Additionally,as the existing data sets of safety helmets are small-sized,which leads to the underfitting of the network,samples of safety helmets are collected from the actual building work videos to construct a suitable sample set.The experimental results show that the proposed algorithm provides a detection speed that is 10.2 times higher than that of the SSD algorithm with the cost of a minor loss in accuracy.

关 键 词：安全帽佩戴检测 轻量型SSD算法 深度学习 检测精度 检测速度 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]