Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessary for administrators to identify the people that do not wear hardhats and send out alarms to them. As manual inspection is labor-intensive and expensive, it is ideal to handle this issue by a real-time automatic detector. As such, in this paper, we present an end-to-end convolutional neural network to solve the problem of detecting if workers are wearing hardhats. The proposed method focuses on localizing a person's head and deciding whether they are wearing a hardhat. The MobileNet model is employed as the backbone network, which allows the detector to run in real time. A top-down module is leveraged to enhance the feature-extraction process. Finally, heads with and without hardhats are detected on multi-scale features using a residual-block-based prediction module. Experimental results on a dataset that we have established show that the proposed method could produce an average precision of 87.4%/89.4% at 62 frames per second for detecting people without/with a hardhat worn on the head.

中文翻译：

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基于具有多尺度特征和自上而下模块的轻型卷积神经网络的安全帽佩戴检测。

由于工人与设备，建筑材料，车辆等的复杂交互作用，施工现场非常危险。安全帽作为一种防护装备，对于施工现场人员的安全至关重要。因此，管理员必须识别不戴安全帽的人员，并向他们发出警报。由于手动检查是劳动密集型且昂贵的，因此理想的是通过实时自动检测器处理此问题。因此，在本文中，我们提出了一种端到端的卷积神经网络，以解决检测工人是否戴着安全帽的问题。所提出的方法着重于定位人的头部并确定他们是否戴着安全帽。MobileNet模型被用作骨干网络，该网络允许检测器实时运行。利用自上而下的模块来增强功能提取过程。最后，使用基于残差块的预测模块在多尺度特征上检测具有和不具有安全帽的头部。我们建立的数据集上的实验结果表明，该方法可以以每秒62帧的速度产生87.4％/ 89.4％的平均精度，用于检测戴/不戴安全帽的人员。