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首页> 外文会议>International Conference on Electrical, Communication and Computer Engineering >Multi-Scale Safety Hardhat Wearing Detection using Deep Learning: A Top-Down and Bottom-Up Module
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Multi-Scale Safety Hardhat Wearing Detection using Deep Learning: A Top-Down and Bottom-Up Module

机译：使用深度学习的多尺度安全性安全帽磨损检测：自上而下和自下而上的模块

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Construction sites are the most unsafe and risky places where thousands of workers are injured and die every year throughout the world. Some protective gear like hardhat can protect personnel from unexpected accidents. Administrators need to confirm all personnel put on hardhat on their heads during working time. However, it is inefficient and time-consuming to monitor this task manually. Hence, an automatic system may give convenience to detect personnel whether they wearing hardhat or not when they are on duty. RatinaNet is used to detect and localize the hardhat/head of personnel into the construction site. ResNet50+Feature Pyramid Network (FPN) is used as the backbone of the architecture, a classification and a regression sun-module are used to classifying objects and localizing bounding box around the object. A robust semantical description is achieved using both top-down pathways and lateral connections. Hardhats or heads are detected on a multiscale using the bottom-up and top-down modules. Experimental analysis on a dataset using RatinaNet produces a prominent result that may be usable in real-time applications.
机译：建筑工地是最不安全和风险的地方，成千上万的工人每年在全世界受伤和死亡。一些像安全帽一样的保护齿轮可以保护人员免受意想不到的事故。管理员需要在工作时间内确认所有人员在其头上放在Hardhat上。但是，手动监控此任务是效率低下且耗时的。因此，自动系统可以提供便利，以检测人员是否佩戴安全帽，或者当它们值班时。 RatinAnet用于检测和定位人员的安全帽/头部进入施工现场。 RESET50 +功能金字塔网络（FPN）用作架构的骨干，分类和回归太阳模块用于对对象周围分类对象和本地化边界框。使用自上而下的路径和横向连接来实现强大的语义描述。使用自下而上和自上而下的模块在多尺度上检测到硬汉或头部。使用RATINANET的数据集的实验分析产生了一个突出的结果，可以在实时应用中使用。

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